polygraf-ai/ACL-abstracts · Datasets at Hugging Face

inproceedings

welivita-pu-2022-curating

Curating a Large-Scale Motivational Interviewing Dataset Using Peer Support Forums

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.293/

Welivita, Anuradha and Pu, Pearl

Proceedings of the 29th International Conference on Computational Linguistics

3315--3330

A significant limitation in developing therapeutic chatbots to support people going through psychological distress is the lack of high-quality, large-scale datasets capturing conversations between clients and trained counselors. As a remedy, researchers have focused their attention on scraping conversational data from peer support platforms such as Reddit. But the extent to which the responses from peers align with responses from trained counselors is understudied. We address this gap by analyzing the differences between responses from counselors and peers by getting trained counselors to annotate {\ensuremath{\approx}}17K such responses using Motivational Interviewing Treatment Integrity (MITI) code, a well-established behavioral coding system that differentiates between favorable and unfavorable responses. We developed an annotation pipeline with several stages of quality control. Due to its design, this method was able to achieve 97{\%} of coverage, meaning that out of the 17.3K responses we successfully labeled 16.8K with a moderate agreement. We use this data to conclude the extent to which conversational data from peer support platforms align with real therapeutic conversations and discuss in what ways they can be exploited to train therapeutic chatbots.

null

28,737

inproceedings

wang-etal-2022-cctc

{CCTC}: A Cross-Sentence {C}hinese Text Correction Dataset for Native Speakers

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.294/

Wang, Baoxin and Duan, Xingyi and Wu, Dayong and Che, Wanxiang and Chen, Zhigang and Hu, Guoping

Proceedings of the 29th International Conference on Computational Linguistics

3331--3341

The Chinese text correction (CTC) focuses on detecting and correcting Chinese spelling errors and grammatical errors. Most existing datasets of Chinese spelling check (CSC) and Chinese grammatical error correction (GEC) are focused on a single sentence written by Chinese-as-a-second-language (CSL) learners. We find that errors caused by native speakers differ significantly from those produced by non-native speakers. These differences make it inappropriate to use the existing test sets directly to evaluate text correction systems for native speakers. Some errors also require the cross-sentence information to be identified and corrected. In this paper, we propose a cross-sentence Chinese text correction dataset for native speakers. Concretely, we manually annotated 1,500 texts written by native speakers. The dataset consists of 30,811 sentences and more than 1,000,000 Chinese characters. It contains four types of errors: spelling errors, redundant words, missing words, and word ordering errors. We also test some state-of-the-art models on the dataset. The experimental results show that even the model with the best performance is 20 points lower than humans, which indicates that there is still much room for improvement. We hope that the new dataset can fill the gap in cross-sentence text correction for native Chinese speakers.

null

28,738

inproceedings

xu-etal-2022-realmeddial

{R}eal{M}ed{D}ial: A Real Telemedical Dialogue Dataset Collected from Online {C}hinese Short-Video Clips

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.295/

Xu, Bo and Zhang, Hongtong and Wang, Jian and Zhang, Xiaokun and Hao, Dezhi and Zong, Linlin and Lin, Hongfei and Ma, Fenglong

Proceedings of the 29th International Conference on Computational Linguistics

3342--3352

Intelligent medical services have attracted great research interests for providing automated medical consultation. However, the lack of corpora becomes a main obstacle to related research, particularly data from real scenarios. In this paper, we construct RealMedDial, a Chinese medical dialogue dataset based on real medical consultation. RealMedDial contains 2,637 medical dialogues and 24,255 utterances obtained from Chinese short-video clips of real medical consultations. We collected and annotated a wide range of meta-data with respect to medical dialogue including doctor profiles, hospital departments, diseases and symptoms for fine-grained analysis on language usage pattern and clinical diagnosis. We evaluate the performance of medical response generation, department routing and doctor recommendation on RealMedDial. Results show that RealMedDial are applicable to a wide range of NLP tasks with respect to medical dialogue.

null

28,739

inproceedings

loureiro-etal-2022-tempowic

{T}empo{W}i{C}: An Evaluation Benchmark for Detecting Meaning Shift in Social Media

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.296/

Loureiro, Daniel and D{'}Souza, Aminette and Muhajab, Areej Nasser and White, Isabella A. and Wong, Gabriel and Espinosa-Anke, Luis and Neves, Leonardo and Barbieri, Francesco and Camacho-Collados, Jose

Proceedings of the 29th International Conference on Computational Linguistics

3353--3359

Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media.

null

28,740

inproceedings

huryn-etal-2022-automatic

Automatic Generation of Large-scale Multi-turn Dialogues from {R}eddit

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.297/

Huryn, Daniil and Hutsell, William M. and Choi, Jinho D.

Proceedings of the 29th International Conference on Computational Linguistics

3360--3373

This paper presents novel methods to automatically convert posts and their comments from discussion forums such as Reddit into multi-turn dialogues. Our methods are generalizable to any forums; thus, they allow us to generate a massive amount of dialogues for diverse topics that can be used to pretrain language models. Four methods are introduced, Greedy{\_}Baseline, Greedy{\_}Advanced, Beam Search and Threading, which are applied to posts from 10 subreddits and assessed. Each method makes a noticeable improvement over its predecessor such that the best method shows an improvement of 36.3{\%} over the baseline for appropriateness. Our best method is applied to posts from those 10 subreddits for the creation of a corpus comprising 10,098 dialogues (3.3M tokens), 570 of which are compared against dialogues in three other datasets, Blended Skill Talk, Daily Dialogue, and Topical Chat. Our dialogues are found to be more engaging but slightly less natural than the ones in the other datasets, while it costs a fraction of human labor and money to generate our corpus compared to the others. To the best of our knowledge, it is the first work to create a large multi-turn dialogue corpus from Reddit that can advance neural dialogue systems.

null

28,741

inproceedings

zhu-etal-2022-configure

{C}on{F}igu{R}e: Exploring Discourse-level {C}hinese Figures of Speech

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.298/

Zhu, Dawei and Zhan, Qiusi and Zhou, Zhejian and Song, Yifan and Zhang, Jiebin and Li, Sujian

Proceedings of the 29th International Conference on Computational Linguistics

3374--3385

Figures of speech, such as metaphor and irony, are ubiquitous in literature works and colloquial conversations. This poses great challenge for natural language understanding since figures of speech usually deviate from their ostensible meanings to express deeper semantic implications. Previous research lays emphasis on the literary aspect of figures and seldom provide a comprehensive exploration from a view of computational linguistics. In this paper, we first propose the concept of figurative unit, which is the carrier of a figure. Then we select 12 types of figures commonly used in Chinese, and build a Chinese corpus for Contextualized Figure Recognition (ConFiguRe). Different from previous token-level or sentence-level counterparts, ConFiguRe aims at extracting a figurative unit from discourse-level context, and classifying the figurative unit into the right figure type. On ConFiguRe, three tasks, i.e., figure extraction, figure type classification and figure recognition, are designed and the state-of-the-art techniques are utilized to implement the benchmarks. We conduct thorough experiments and show that all three tasks are challenging for existing models, thus requiring further research. Our dataset and code are publicly available at \url{https://github.com/pku-tangent/ConFiguRe}.

null

28,742

inproceedings

antypas-etal-2022-twitter

{T}witter Topic Classification

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.299/

Antypas, Dimosthenis and Ushio, Asahi and Camacho-Collados, Jose and Silva, Vitor and Neves, Leonardo and Barbieri, Francesco

Proceedings of the 29th International Conference on Computational Linguistics

3386--3400

Social media platforms host discussions about a wide variety of topics that arise everyday. Making sense of all the content and organising it into categories is an arduous task. A common way to deal with this issue is relying on topic modeling, but topics discovered using this technique are difficult to interpret and can differ from corpus to corpus. In this paper, we present a new task based on tweet topic classification and release two associated datasets. Given a wide range of topics covering the most important discussion points in social media, we provide training and testing data from recent time periods that can be used to evaluate tweet classification models. Moreover, we perform a quantitative evaluation and analysis of current general- and domain-specific language models on the task, which provide more insights on the challenges and nature of the task.

null

28,743

inproceedings

vu-etal-2022-layer

Layer or Representation Space: What Makes {BERT}-based Evaluation Metrics Robust?

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.300/

Vu, Doan Nam Long and Moosavi, Nafise Sadat and Eger, Steffen

Proceedings of the 29th International Conference on Computational Linguistics

3401--3411

The evaluation of recent embedding-based evaluation metrics for text generation is primarily based on measuring their correlation with human evaluations on standard benchmarks. However, these benchmarks are mostly from similar domains to those used for pretraining word embeddings. This raises concerns about the (lack of) generalization of embedding-based metrics to new and noisy domains that contain a different vocabulary than the pretraining data. In this paper, we examine the robustness of BERTScore, one of the most popular embedding-based metrics for text generation. We show that (a) an embedding-based metric that has the highest correlation with human evaluations on a standard benchmark can have the lowest correlation if the amount of input noise or unknown tokens increases, (b) taking embeddings from the first layer of pretrained models improves the robustness of all metrics, and (c) the highest robustness is achieved when using character-level embeddings, instead of token-based embeddings, from the first layer of the pretrained model.

null

28,744

inproceedings

liu-etal-2022-evaluating

Evaluating the Performance of Transformer-based Language Models for Neuroatypical Language

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.301/

Liu, Duanchen and Liu, Zoey and Yang, Qingyun and Huang, Yujing and Prud{'}hommeaux, Emily

Proceedings of the 29th International Conference on Computational Linguistics

3412--3419

Difficulties with social aspects of language are among the hallmarks of autism spectrum disorder (ASD). These communication differences are thought to contribute to the challenges that adults with ASD experience when seeking employment, underscoring the need for interventions that focus on improving areas of weakness in pragmatic and social language. In this paper, we describe a transformer-based framework for identifying linguistic features associated with social aspects of communication using a corpus of conversations between adults with and without ASD and neurotypical conversational partners produced while engaging in collaborative tasks. While our framework yields strong accuracy overall, performance is significantly worse for the language of participants with ASD, suggesting that they use a more diverse set of strategies for some social linguistic functions. These results, while showing promise for the development of automated language analysis tools to support targeted language interventions for ASD, also reveal weaknesses in the ability of large contextualized language models to model neuroatypical language.

null

28,745

inproceedings

bruches-etal-2022-terminator

{TERM}inator: A System for Scientific Texts Processing

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.302/

Bruches, Elena and Tikhobaeva, Olga and Dementyeva, Yana and Batura, Tatiana

Proceedings of the 29th International Conference on Computational Linguistics

3420--3426

This paper is devoted to the extraction of entities and semantic relations between them from scientific texts, where we consider scientific terms as entities. In this paper, we present a dataset that includes annotations for two tasks and develop a system called TERMinator for the study of the influence of language models on term recognition and comparison of different approaches for relation extraction. Experiments show that language models pre-trained on the target language are not always show the best performance. Also adding some heuristic approaches may improve the overall quality of the particular task. The developed tool and the annotated corpus are publicly available at \url{https://github.com/iis-research-team/terminator} and may be useful for other researchers.

null

28,746

inproceedings

koto-etal-2022-lipkey

{L}ip{K}ey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.303/

Koto, Fajri and Baldwin, Timothy and Lau, Jey Han

Proceedings of the 29th International Conference on Computational Linguistics

3427--3437

Summaries, keyphrases, and titles are different ways of concisely capturing the content of a document. While most previous work has released the datasets of keyphrases and summarization separately, in this work, we introduce LipKey, the largest news corpus with human-written abstractive summaries, absent keyphrases, and titles. We jointly use the three elements via multi-task training and training as joint structured inputs, in the context of document summarization. We find that including absent keyphrases and titles as additional context to the source document improves transformer-based summarization models.

null

28,747

inproceedings

cao-etal-2022-understanding

Understanding Attention for Vision-and-Language Tasks

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.304/

Cao, Feiqi and Han, Soyeon Caren and Long, Siqu and Xu, Changwei and Poon, Josiah

Proceedings of the 29th International Conference on Computational Linguistics

3438--3453

Attention mechanism has been used as an important component across Vision-and-Language(VL) tasks in order to bridge the semantic gap between visual and textual features. While attention has been widely used in VL tasks, it has not been examined the capability of different attention alignment calculation in bridging the semantic gap between visual and textual clues. In this research, we conduct a comprehensive analysis on understanding the role of attention alignment by looking into the attention score calculation methods and check how it actually represents the visual region`s and textual token`s significance for the global assessment. We also analyse the conditions which attention score calculation mechanism would be more (or less) interpretable, and which may impact the model performance on three different VL tasks, including visual question answering, text-to-image generation, text-and-image matching (both sentence and image retrieval). Our analysis is the first of its kind and provides useful insights of the importance of each attention alignment score calculation when applied at the training phase of VL tasks, commonly ignored in attention-based cross modal models, and/or pretrained models. Our code is available at: \url{https://github.com/adlnlp/Attention_VL}

null

28,748

inproceedings

piedboeuf-langlais-2022-effective

Effective Data Augmentation for Sentence Classification Using One {VAE} per Class

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.305/

Piedboeuf, Fr{\'e}d{\'e}ric and Langlais, Philippe

Proceedings of the 29th International Conference on Computational Linguistics

3454--3464

In recent years, data augmentation has become an important field of machine learning. While images can use simple techniques such as cropping or rotating, textual data augmentation needs more complex manipulations to ensure that the generated examples are useful. Variational auto-encoders (VAE) and its conditional variant the Conditional-VAE (CVAE) are often used to generate new textual data, both relying on a good enough training of the generator so that it doesn`t create examples of the wrong class. In this paper, we explore a simpler way to use VAE for data augmentation: the training of one VAE per class. We show on several dataset sizes, as well as on four different binary classification tasks, that it systematically outperforms other generative data augmentation techniques.

null

28,749

inproceedings

frisoni-etal-2022-nlg

{NLG}-Metricverse: An End-to-End Library for Evaluating Natural Language Generation

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.306/

Frisoni, Giacomo and Carbonaro, Antonella and Moro, Gianluca and Zammarchi, Andrea and Avagnano, Marco

Proceedings of the 29th International Conference on Computational Linguistics

3465--3479

Driven by deep learning breakthroughs, natural language generation (NLG) models have been at the center of steady progress in the last few years, with a ubiquitous task influence. However, since our ability to generate human-indistinguishable artificial text lags behind our capacity to assess it, it is paramount to develop and apply even better automatic evaluation metrics. To facilitate researchers to judge the effectiveness of their models broadly, we introduce NLG-Metricverse{---}an end-to-end open-source library for NLG evaluation based on Python. Our framework provides a living collection of NLG metrics in a unified and easy-to-use environment, supplying tools to efficiently apply, analyze, compare, and visualize them. This includes (i) the extensive support to heterogeneous automatic metrics with n-arity management, (ii) the meta-evaluation upon individual performance, metric-metric and metric-human correlations, (iii) graphical interpretations for helping humans better gain score intuitions, (iv) formal categorization and convenient documentation to accelerate metrics understanding. NLG-Metricverse aims to increase the comparability and replicability of NLG research, hopefully stimulating new contributions in the area.

null

28,750

inproceedings

yang-etal-2022-testaug

{T}est{A}ug: A Framework for Augmenting Capability-based {NLP} Tests

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.307/

Yang, Guanqun and Haque, Mirazul and Song, Qiaochu and Yang, Wei and Liu, Xueqing

Proceedings of the 29th International Conference on Computational Linguistics

3480--3495

The recently proposed capability-based NLP testing allows model developers to test the functional capabilities of NLP models, revealing functional failures for models with good held-out evaluation scores. However, existing work on capability-based testing requires the developer to compose each individual test template from scratch. Such approach thus requires extensive manual efforts and is less scalable. In this paper, we investigate a different approach that requires the developer to only annotate a few test templates, while leveraging the GPT-3 engine to generate the majority of test cases. While our approach saves the manual efforts by design, it guarantees the correctness of the generated suites with a validity checker. Moreover, our experimental results show that the test suites generated by GPT-3 are more diverse than the manually created ones; they can also be used to detect more errors compared to manually created counterparts. Our test suites can be downloaded at \url{https://anonymous-researcher-nlp.github.io/testaug/}.

null

28,751

inproceedings

kim-etal-2022-kochet

{K}o{CHET}: A {K}orean Cultural Heritage Corpus for Entity-related Tasks

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.308/

Kim, Gyeongmin and Kim, Jinsung and Son, Junyoung and Lim, Heuiseok

Proceedings of the 29th International Conference on Computational Linguistics

3496--3505

As digitized traditional cultural heritage documents have rapidly increased, resulting in an increased need for preservation and management, practical recognition of entities and typification of their classes has become essential. To achieve this, we propose KoCHET - a Korean cultural heritage corpus for the typical entity-related tasks, i.e., named entity recognition (NER), relation extraction (RE), and entity typing (ET). Advised by cultural heritage experts based on the data construction guidelines of government-affiliated organizations, KoCHET consists of respectively 112,362, 38,765, 113,198 examples for NER, RE, and ET tasks, covering all entity types related to Korean cultural heritage. Moreover, unlike the existing public corpora, modified redistribution can be allowed both domestic and foreign researchers. Our experimental results make the practical usability of KoCHET more valuable in terms of cultural heritage. We also provide practical insights of KoCHET in terms of statistical and linguistic analysis. Our corpus is freely available at \url{https://github.com/Gyeongmin47/KoCHET}.

null

28,752

inproceedings

abonizio-etal-2022-monobyte

{M}ono{B}yte: A Pool of Monolingual Byte-level Language Models

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.309/

Abonizio, Hugo and de Souza, Leandro Rodrigues and Lotufo, Roberto and Nogueira, Rodrigo

Proceedings of the 29th International Conference on Computational Linguistics

3506--3513

The zero-shot cross-lingual ability of models pretrained on multilingual and even monolingual corpora has spurred many hypotheses to explain this intriguing empirical result. However, due to the costs of pretraining, most research uses public models whose pretraining methodology, such as the choice of tokenization, corpus size, and computational budget, might differ drastically. When researchers pretrain their own models, they often do so under a constrained budget, and the resulting models might underperform significantly compared to SOTA models. These experimental differences led to various inconsistent conclusions about the nature of the cross-lingual ability of these models. To help further research on the topic, we released 10 monolingual byte-level models rigorously pretrained under the same configuration with a large compute budget (equivalent to 420 days on a V100) and corpora that are 4 times larger than the original BERT`s. Because they are tokenizer-free, the problem of unseen token embeddings is eliminated, thus allowing researchers to try a wider range of cross-lingual experiments in languages with different scripts. Additionally, we release two models pretrained on non-natural language texts that can be used in sanity-check experiments. Experiments on QA and NLI tasks show that our monolingual models achieve competitive performance to the multilingual one, and hence can be served to strengthen our understanding of cross-lingual transferability in language models.

null

28,753

inproceedings

choi-etal-2022-wizard

Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.310/

Choi, Jason Ingyu and Kuzi, Saar and Vedula, Nikhita and Zhao, Jie and Castellucci, Giuseppe and Collins, Marcus and Malmasi, Shervin and Rokhlenko, Oleg and Agichtein, Eugene

Proceedings of the 29th International Conference on Computational Linguistics

3514--3529

Conversational Task Assistants (CTAs) are conversational agents whose goal is to help humans perform real-world tasks. CTAs can help in exploring available tasks, answering task-specific questions and guiding users through step-by-step instructions. In this work, we present Wizard of Tasks, the first corpus of such conversations in two domains: Cooking and Home Improvement. We crowd-sourced a total of 549 conversations (18,077 utterances) with an asynchronous Wizard-of-Oz setup, relying on recipes from WholeFoods Market for the cooking domain, and WikiHow articles for the home improvement domain. We present a detailed data analysis and show that the collected data can be a valuable and challenging resource for CTAs in two tasks: Intent Classification (IC) and Abstractive Question Answering (AQA). While on IC we acquired a high performing model ({\ensuremath{>}}85{\%} F1), on AQA the performance is far from being satisfactory ({\textasciitilde}27{\%} BertScore-F1), suggesting that more work is needed to solve the task of low-resource AQA.

null

28,754

inproceedings

lee-etal-2022-k

K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.311/

Lee, Jean and Lim, Taejun and Lee, Heejun and Jo, Bogeun and Kim, Yangsok and Yoon, Heegeun and Han, Soyeon Caren

Proceedings of the 29th International Conference on Computational Linguistics

3530--3538

Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides a multi-label classification using 1 to 4 labels, and handles subjectivity and intersectionality. We evaluate strong baselines on K-MHaS. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class.

null

28,755

inproceedings

buhmann-etal-2022-domain

Domain- and Task-Adaptation for {V}accin{C}hat{NL}, a {D}utch {COVID}-19 {FAQ} Answering Corpus and Classification Model

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.312/

Buhmann, Jeska and De Bruyn, Maxime and Lotfi, Ehsan and Daelemans, Walter

Proceedings of the 29th International Conference on Computational Linguistics

3539--3549

FAQs are important resources to find information. However, especially if a FAQ concerns many question-answer pairs, it can be a difficult and time-consuming job to find the answer you are looking for. A FAQ chatbot can ease this process by automatically retrieving the relevant answer to a user`s question. We present VaccinChatNL, a Dutch FAQ corpus on the topic of COVID-19 vaccination. Starting with 50 question-answer pairs we built VaccinChat, a FAQ chatbot, which we used to gather more user questions that were also annotated with the appropriate or new answer classes. This iterative process of gathering user questions, annotating them, and retraining the model with the increased data set led to a corpus that now contains 12,883 user questions divided over 181 answers. We provide the first publicly available Dutch FAQ answering data set of this size with large groups of semantically equivalent human-paraphrased questions. Furthermore, our study shows that before fine-tuning a classifier, continued pre-training of Dutch language models with task- and/or domain-specific data improves classification results. In addition, we show that large groups of semantically similar questions are important for obtaining well-performing intent classification models.

null

28,756

inproceedings

luo-etal-2022-benchmarking

Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.313/

Luo, Junyu and Lin, Junxian and Lin, Chi and Xiao, Cao and Gui, Xinning and Ma, Fenglong

Proceedings of the 29th International Conference on Computational Linguistics

3550--3562

Patients with low health literacy usually have difficulty understanding medical jargon and the complex structure of professional medical language. Although some studies are proposed to automatically translate expert language into layperson-understandable language, only a few of them focus on both accuracy and readability aspects simultaneously in the clinical domain. Thus, simplification of the clinical language is still a challenging task, but unfortunately, it is not yet fully addressed in previous work. To benchmark this task, we construct a new dataset named MedLane to support the development and evaluation of automated clinical language simplification approaches. Besides, we propose a new model called DECLARE that follows the human annotation procedure and achieves state-of-the-art performance compared with eight strong baselines. To fairly evaluate the performance, we also propose three specific evaluation metrics. Experimental results demonstrate the utility of the annotated MedLane dataset and the effectiveness of the proposed model DECLARE.

null

28,757

inproceedings

chang-etal-2022-wikihan

{W}iki{H}an: A New Comparative Dataset for {C}hinese Languages

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.314/

Chang, Kalvin and Cui, Chenxuan and Kim, Youngmin and Mortensen, David R.

Proceedings of the 29th International Conference on Computational Linguistics

3563--3569

Most comparative datasets of Chinese varieties are not digital; however, Wiktionary includes a wealth of transcriptions of words from these varieties. The usefulness of these data is limited by the fact that they use a wide range of variety-specific romanizations, making data difficult to compare. The current work collects this data into a single constituent (IPA, or International Phonetic Alphabet) and structured form (TSV) for use in comparative linguistics and Chinese NLP. At the time of writing, the dataset contains 67,943 entries across 8 varieties and Middle Chinese. The dataset is validated on a protoform reconstruction task using an encoder-decoder cross-attention architecture (Meloni et al 2021), achieving an accuracy of 54.11{\%}, a PER (phoneme error rate) of 17.69{\%}, and a FER (feature error rate) of 6.60{\%}.

null

28,758

inproceedings

shirai-etal-2022-visual

Visual Recipe Flow: A Dataset for Learning Visual State Changes of Objects with Recipe Flows

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.315/

Shirai, Keisuke and Hashimoto, Atsushi and Nishimura, Taichi and Kameko, Hirotaka and Kurita, Shuhei and Ushiku, Yoshitaka and Mori, Shinsuke

Proceedings of the 29th International Conference on Computational Linguistics

3570--3577

We present a new multimodal dataset called Visual Recipe Flow, which enables us to learn a cooking action result for each object in a recipe text. The dataset consists of object state changes and the workflow of the recipe text. The state change is represented as an image pair, while the workflow is represented as a recipe flow graph. We developed a web interface to reduce human annotation costs. The dataset allows us to try various applications, including multimodal information retrieval.

null

28,759

inproceedings

maeda-etal-2022-impara

{IMPARA}: Impact-Based Metric for {GEC} Using Parallel Data

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.316/

Maeda, Koki and Kaneko, Masahiro and Okazaki, Naoaki

Proceedings of the 29th International Conference on Computational Linguistics

3578--3588

Automatic evaluation of grammatical error correction (GEC) is essential in developing useful GEC systems. Existing methods for automatic evaluation require multiple reference sentences or manual scores. However, such resources are expensive, thereby hindering automatic evaluation for various domains and correction styles. This paper proposes an Impact-based Metric for GEC using PARAllel data, IMPARA, which utilizes correction impacts computed by parallel data comprising pairs of grammatical/ungrammatical sentences. As parallel data is cheaper than manually assessing evaluation scores, IMPARA can reduce the cost of data creation for automatic evaluation. Correlations between IMPARA and human scores indicate that IMPARA is comparable or better than existing evaluation methods. Furthermore, we find that IMPARA can perform evaluations that fit different domains and correction styles trained on various parallel data.

null

28,760

inproceedings

krstovski-etal-2022-evons

Evons: A Dataset for Fake and Real News Virality Analysis and Prediction

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.317/

Krstovski, Kriste and Ryu, Angela Soomin and Kogut, Bruce

Proceedings of the 29th International Conference on Computational Linguistics

3589--3596

We present a novel collection of news articles originating from fake and real news media sources for the analysis and prediction of news virality. Unlike existing fake news datasets which either contain claims, or news article headline and body, in this collection each article is supported with a Facebook engagement count which we consider as an indicator of the article virality. In addition we also provide the article description and thumbnail image with which the article was shared on Facebook. These images were automatically annotated with object tags and color attributes. Using cloud based vision analysis tools, thumbnail images were also analyzed for faces and detected faces were annotated with facial attributes. We empirically investigate the use of this collection on an example task of article virality prediction.

null

28,761

inproceedings

cabello-piqueras-sogaard-2022-pretrained

Are Pretrained Multilingual Models Equally Fair across Languages?

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.318/

Cabello Piqueras, Laura and S{\o}gaard, Anders

Proceedings of the 29th International Conference on Computational Linguistics

3597--3605

Pretrained multilingual language models can help bridge the digital language divide, enabling high-quality NLP models for lower-resourced languages. Studies of multilingual models have so far focused on performance, consistency, and cross-lingual generalisation. However, with their wide-spread application in the wild and downstream societal impact, it is important to put multilingual models under the same scrutiny as monolingual models. This work investigates the group fairness of multilingual models, asking whether these models are equally fair across languages. To this end, we create a new four-way multilingual dataset of parallel cloze test examples (MozArt), equipped with demographic information (balanced with regard to gender and native tongue) about the test participants. We evaluate three multilingual models on MozArt {--}mBERT, XLM-R, and mT5{--} and show that across the four target languages, the three models exhibit different levels of group disparity, e.g., exhibiting near-equal risk for Spanish, but high levels of disparity for German.

null

28,762

inproceedings

ashida-sugawara-2022-possible

Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.319/

Ashida, Mana and Sugawara, Saku

Proceedings of the 29th International Conference on Computational Linguistics

3606--3630

The possible consequences for the same context may vary depending on the situation we refer to. However, current studies in natural language processing do not focus on situated commonsense reasoning under multiple possible scenarios. This study frames this task by asking multiple questions with the same set of possible endings as candidate answers, given a short story text. Our resulting dataset, Possible Stories, consists of more than 4.5K questions over 1.3K story texts in English. We discover that even current strong pretrained language models struggle to answer the questions consistently, highlighting that the highest accuracy in an unsupervised setting (60.2{\%}) is far behind human accuracy (92.5{\%}). Through a comparison with existing datasets, we observe that the questions in our dataset contain minimal annotation artifacts in the answer options. In addition, our dataset includes examples that require counterfactual reasoning, as well as those requiring readers' reactions and fictional information, suggesting that our dataset can serve as a challenging testbed for future studies on situated commonsense reasoning.

null

28,763

inproceedings

oleksy-etal-2022-diabiz

{D}ia{B}iz.{K}om - towards a {P}olish Dialogue Act Corpus Based on {ISO} 24617-2 Standard

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.320/

Oleksy, Marcin and Wieczorek, Jan and Dru{\.z}y{\l}owska, Dorota and Klyus, Julia and Domoga{\l}a, Aleksandra and Hwaszcz, Krzysztof and K{\k{e}}dzierska, Hanna and Miko{\'s}, Daria and Wr{\'o}{\.z}, Anita

Proceedings of the 29th International Conference on Computational Linguistics

3631--3638

This article presents the specification and evaluation of DiaBiz.Kom {--} the corpus of dialogue texts in Polish. The corpus contains transcriptions of telephone conversations conducted according to a prepared scenario. The transcripts of conversations have been manually annotated with a layer of information concerning communicative functions. DiaBiz.Kom is the first corpus of this type prepared for the Polish language and will be used to develop a system of dialog analysis and modules for creating advanced chatbots.

null

28,764

inproceedings

lymperaiou-etal-2022-towards

Towards Explainable Evaluation of Language Models on the Semantic Similarity of Visual Concepts

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.321/

Lymperaiou, Maria and Manoliadis, George and Menis Mastromichalakis, Orfeas and Dervakos, Edmund G. and Stamou, Giorgos

Proceedings of the 29th International Conference on Computational Linguistics

3639--3658

Recent breakthroughs in NLP research, such as the advent of Transformer models have indisputably contributed to major advancements in several tasks. However, few works research robustness and explainability issues of their evaluation strategies. In this work, we examine the behavior of high-performing pre-trained language models, focusing on the task of semantic similarity for visual vocabularies. First, we address the need for explainable evaluation metrics, necessary for understanding the conceptual quality of retrieved instances. Our proposed metrics provide valuable insights in local and global level, showcasing the inabilities of widely used approaches. Secondly, adversarial interventions on salient query semantics expose vulnerabilities of opaque metrics and highlight patterns in learned linguistic representations.

null

28,765

inproceedings

marchal-etal-2022-establishing

Establishing Annotation Quality in Multi-label Annotations

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.322/

Marchal, Marian and Scholman, Merel and Yung, Frances and Demberg, Vera

Proceedings of the 29th International Conference on Computational Linguistics

3659--3668

In many linguistic fields requiring annotated data, multiple interpretations of a single item are possible. Multi-label annotations more accurately reflect this possibility. However, allowing for multi-label annotations also affects the chance that two coders agree with each other. Calculating inter-coder agreement for multi-label datasets is therefore not trivial. In the current contribution, we evaluate different metrics for calculating agreement on multi-label annotations: agreement on the intersection of annotated labels, an augmented version of Cohen`s Kappa, and precision, recall and F1. We propose a bootstrapping method to obtain chance agreement for each measure, which allows us to obtain an adjusted agreement coefficient that is more interpretable. We demonstrate how various measures affect estimates of agreement on simulated datasets and present a case study of discourse relation annotations. We also show how the proportion of double labels, and the entropy of the label distribution, influences the measures outlined above and how a bootstrapped adjusted agreement can make agreement measures more comparable across datasets in multi-label scenarios.

null

28,766

inproceedings

wiegand-etal-2022-biographically

Biographically Relevant Tweets {--} a New Dataset, Linguistic Analysis and Classification Experiments

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.323/

Wiegand, Michael and Wilm, Rebecca and Markert, Katja

Proceedings of the 29th International Conference on Computational Linguistics

3669--3679

We present a new dataset comprising tweets for the novel task of detecting biographically relevant utterances. Biographically relevant utterances are all those utterances that reveal some persistent and non-trivial information about the author of a tweet, e.g. habits, (dis)likes, family status, physical appearance, employment information, health issues etc. Unlike previous research we do not restrict biographical relevance to a small fixed set of pre-defined relations. Next to classification experiments employing state-of-the-art classifiers to establish strong baselines for future work, we carry out a linguistic analysis that compares the predictiveness of various high-level features. We also show that the task is different from established tasks, such as aspectual classification or sentiment analysis.

null

28,767

inproceedings

jang-etal-2022-becel

{BECEL}: Benchmark for Consistency Evaluation of Language Models

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.324/

Jang, Myeongjun and Kwon, Deuk Sin and Lukasiewicz, Thomas

Proceedings of the 29th International Conference on Computational Linguistics

3680--3696

Behavioural consistency is a critical condition for a language model (LM) to become trustworthy like humans. Despite its importance, however, there is little consensus on the definition of LM consistency, resulting in different definitions across many studies. In this paper, we first propose the idea of LM consistency based on behavioural consistency and establish a taxonomy that classifies previously studied consistencies into several sub-categories. Next, we create a new benchmark that allows us to evaluate a model on 19 test cases, distinguished by multiple types of consistency and diverse downstream tasks. Through extensive experiments on the new benchmark, we ascertain that none of the modern pre-trained language models (PLMs) performs well in every test case, while exhibiting high inconsistency in many cases. Our experimental results suggest that a unified benchmark that covers broad aspects (i.e., multiple consistency types and tasks) is essential for a more precise evaluation.

null

28,768

inproceedings

jang-etal-2022-kobest

{K}o{BEST}: {K}orean Balanced Evaluation of Significant Tasks

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.325/

Jang, Myeongjun and Kim, Dohyung and Kwon, Deuk Sin and Davis, Eric

Proceedings of the 29th International Conference on Computational Linguistics

3697--3708

A well-formulated benchmark plays a critical role in spurring advancements in the natural language processing (NLP) field, as it allows objective and precise evaluation of diverse models. As modern language models (LMs) have become more elaborate and sophisticated, more difficult benchmarks that require linguistic knowledge and reasoning have been proposed. However, most of these benchmarks only support English, and great effort is necessary to construct benchmarks for other low resource languages. To this end, we propose a new benchmark named Korean balanced evaluation of significant tasks (KoBEST), which consists of five Korean-language downstream tasks. Professional Korean linguists designed the tasks that require advanced Korean linguistic knowledge. Moreover, our data is purely annotated by humans and thoroughly reviewed to guarantee high data quality. We also provide baseline models and human performance results. Our dataset is available on the Huggingface.

null

28,769

inproceedings

landes-etal-2022-new

A New Public Corpus for Clinical Section Identification: {M}ed{S}ec{I}d

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.326/

Landes, Paul and Patel, Kunal and Huang, Sean S. and Webb, Adam and Di Eugenio, Barbara and Caragea, Cornelia

Proceedings of the 29th International Conference on Computational Linguistics

3709--3721

The process by which sections in a document are demarcated and labeled is known as section identification. Such sections are helpful to the reader when searching for information and contextualizing specific topics. The goal of this work is to segment the sections of clinical medical domain documentation. The primary contribution of this work is MedSecId, a publicly available set of 2,002 fully annotated medical notes from the MIMIC-III. We include several baselines, source code, a pretrained model and analysis of the data showing a relationship between medical concepts across sections using principal component analysis.

null

28,770

inproceedings

ortiz-suarez-gabay-2022-data

A Data-driven Approach to Named Entity Recognition for Early {M}odern {F}rench

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.327/

Ortiz Suarez, Pedro and Gabay, Simon

Proceedings of the 29th International Conference on Computational Linguistics

3722--3730

Named entity recognition has become an increasingly useful tool for digital humanities research, specially when it comes to historical texts. However, historical texts pose a wide range of challenges to both named entity recognition and natural language processing in general that are still difficult to address even with modern neural methods. In this article we focus in named entity recognition for historical French, and in particular for Early Modern French (16th-18th c.), i.e. Ancien R{\'e}gime French. However, instead of developing a specialised architecture to tackle the particularities of this state of language, we opt for a data-driven approach by developing a new corpus with fine-grained entity annotation, covering three centuries of literature corresponding to the early modern period; we try to annotate as much data as possible producing a corpus that is many times bigger than the most popular NER evaluation corpora for both Contemporary English and French. We then fine-tune existing state-of-the-art architectures for Early Modern and Contemporary French, obtaining results that are on par with those of the current state-of-the-art NER systems for Contemporary English. Both the corpus and the fine-tuned models are released.

null

28,771

inproceedings

parameswaran-etal-2022-reproducibility

Reproducibility and Automation of the Appraisal Taxonomy

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.328/

Parameswaran, Pradeesh and Trotman, Andrew and Liesaputra, Veronica and Eyers, David

Proceedings of the 29th International Conference on Computational Linguistics

3731--3740

There is a lack of reproducibility in results from experiments that apply the Appraisal taxonomy. Appraisal is widely used by linguists to study how people judge things or people. Automating Appraisal could be beneficial for use cases such as moderating online comments. Past work in Appraisal annotation has been descriptive in nature and, the lack of publicly available data sets hinders the progress of automation. In this work, we are interested in two things; first, measuring the performance of automated approaches to Appraisal classification in the publicly available Australasian Language Technology Association (ALTA) Shared Task Challenge data set. Second, we are interested in reproducing the annotation of the ALTA data set. Four additional annotators, each with a different linguistics background, were employed to re-annotate the data set. Our results show a poor level of agreement at more detailed Appraisal categories (Fleiss Kappa = 0.059) and a fair level of agreement (Kappa = 0.372) at coarse-level categories. We find similar results when using automated approaches that are available publicly. Our empirical evidence suggests that at present, automating classification is practical only when considering coarse-level categories of the taxonomy.

null

28,772

inproceedings

liu-etal-2022-shot

Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.329/

Liu, Ruixue and Yuan, Shaozu and Dai, Aijun and Shen, Lei and Zhu, Tiangang and Chen, Meng and He, Xiaodong

Proceedings of the 29th International Conference on Computational Linguistics

3741--3752

Few-shot table understanding is a critical and challenging problem in real-world scenario as annotations over large amount of tables are usually costly. Pre-trained language models (PLMs), which have recently flourished on tabular data, have demonstrated their effectiveness for table understanding tasks. However, few-shot table understanding is rarely explored due to the deficiency of public table pre-training corpus and well-defined downstream benchmark tasks, especially in Chinese. In this paper, we establish a benchmark dataset, FewTUD, which consists of 5 different tasks with human annotations to systematically explore the few-shot table understanding in depth. Since there is no large number of public Chinese tables, we also collect a large-scale, multi-domain tabular corpus to facilitate future Chinese table pre-training, which includes one million tables and related natural language text with auxiliary supervised interaction signals. Finally, we present FewTPT, a novel table PLM with rich interactions over tabular data, and evaluate its performance comprehensively on the benchmark. Our dataset and model will be released to the public soon.

null

28,773

inproceedings

ahmed-etal-2022-tafsir

Tafsir Dataset: A Novel Multi-Task Benchmark for Named Entity Recognition and Topic Modeling in Classical {A}rabic Literature

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.330/

Ahmed, Sajawel and van der Goot, Rob and Rehman, Misbahur and Kruse, Carl and {\"Ozsoy, {\"Omer and Mehler, Alexander and Roig, Gemma

Proceedings of the 29th International Conference on Computational Linguistics

3753--3768

Various historical languages, which used to be lingua franca of science and arts, deserve the attention of current NLP research. In this work, we take the first data-driven steps towards this research line for Classical Arabic (CA) by addressing named entity recognition (NER) and topic modeling (TM) on the example of CA literature. We manually annotate the encyclopedic work of Tafsir Al-Tabari with span-based NEs, sentence-based topics, and span-based subtopics, thus creating the Tafsir Dataset with over 51,000 sentences, the first large-scale multi-task benchmark for CA. Next, we analyze our newly generated dataset, which we make open-source available, with current language models (lightweight BiLSTM, transformer-based MaChAmP) along a novel script compression method, thereby achieving state-of-the-art performance for our target task CA-NER. We also show that CA-TM from the perspective of historical topic models, which are central to Arabic studies, is very challenging. With this interdisciplinary work, we lay the foundations for future research on automatic analysis of CA literature.

null

28,774

inproceedings

sekine-etal-2022-resource

Resource of {W}ikipedias in 31 Languages Categorized into Fine-Grained Named Entities

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.331/

Sekine, Satoshi and Nakayama, Kouta and Nomoto, Masako and Ando, Maya and Sumida, Asuka and Matsuda, Koji

Proceedings of the 29th International Conference on Computational Linguistics

3769--3777

This paper describes a resource of Wikipedias in 31 languages categorized into Extended Named Entity (ENE), which has 219 fine-grained NE categories. We first categorized 920 K Japanese Wikipedia pages according to the ENE scheme using machine learning, followed by manual validation. We then organized a shared task of Wikipedia categorization into 30 languages. The training data were provided by Japanese categorization and the language links, and the task was to categorize the Wikipedia pages into 30 languages, with no language links from Japanese Wikipedia (20M pages in total). Thirteen groups with 24 systems participated in the 2020 and 2021 tasks, sharing their outputs for resource-building. The Japanese categorization accuracy was 98.5{\%}, and the best performance among the 30 languages ranges from 80 to 93 in F-measure. Using ensemble learning, we created outputs with an average F-measure of 86.8, which is 1.7 better than the best single systems. The total size of the resource is 32.5M pages, including the training data. We call this resource creation scheme {\textquotedblleft}Resource by Collaborative Contribution (RbCC){\textquotedblright}. We also constructed structuring tasks (attribute extraction and link prediction) using RbCC under our ongoing project, {\textquotedblleft}SHINRA.{\textquotedblright}

null

28,775

inproceedings

raza-etal-2022-accuracy

Accuracy meets Diversity in a News Recommender System

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.332/

Raza, Shaina and Bashir, Syed Raza and Naseem, Usman

Proceedings of the 29th International Conference on Computational Linguistics

3778--3787

News recommender systems face certain challenges. These challenges arise due to evolving users' preferences over dynamically created news articles. The diversity is necessary for a news recommender system to expose users to a variety of information. We propose a deep neural network based on a two-tower architecture that learns news representation through a news item tower and users' representations through a query tower. We customize an augmented vector for each query and news item to introduce information interaction between the two towers. We introduce diversity in the proposed architecture by considering a category loss function that aligns items' representation of uneven news categories. Experimental results on two news datasets reveal that our proposed architecture is more effective compared to the state-of-the-art methods and achieves a balance between accuracy and diversity.

null

28,776

inproceedings

zhang-etal-2022-dynamic

Dynamic Nonlinear Mixup with Distance-based Sample Selection

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.333/

Zhang, Shaokang and Jiang, Lei and Tan, Jianlong

Proceedings of the 29th International Conference on Computational Linguistics

3788--3797

Data augmentation with mixup has shown to be effective on the NLP tasks. Although its great success, the mixup still has shortcomings. First, vanilla mixup randomly selects one sample to generate the mixup sample for a given sample. It remains unclear how to best choose the input samples for the mixup. Second, linear interpolation limits the space of synthetic data and its regularization effect. In this paper, we propose the dynamic nonlinear mixup with distance-based sample selection, which not only generates multiple sample pairs based on the distance between each sample but also enlarges the space of synthetic samples. Specifically, we compute the distance between each input data by cosine similarity and select multiple samples for a given sample. Then we use the dynamic nonlinear mixup to fuse sample pairs. It does not use a linear, scalar mixing strategy, but a nonlinear interpolation strategy, where the mixing strategy is adaptively updated for the input and label pairs. Experiments on the multiple public datasets demonstrate that dynamic nonlinear mixup outperforms state-of-the-art methods.

null

28,777

inproceedings

malmasi-etal-2022-multiconer

{M}ulti{C}o{NER}: A Large-scale Multilingual Dataset for Complex Named Entity Recognition

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.334/

Malmasi, Shervin and Fang, Anjie and Fetahu, Besnik and Kar, Sudipta and Rokhlenko, Oleg

Proceedings of the 29th International Conference on Computational Linguistics

3798--3809

We present AnonData, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions. The 26M token dataset is compiled from public resources using techniques such as heuristic-based sentence sampling, template extraction and slotting, and machine translation. We tested the performance of two NER models on our dataset: a baseline XLM-RoBERTa model, and a state-of-the-art NER GEMNET model that leverages gazetteers. The baseline achieves moderate performance (macro-F1=54{\%}). GEMNET, which uses gazetteers, improvement significantly (average improvement of macro-F1=+30{\%}) and demonstrates the difficulty of our dataset. AnonData poses challenges even for large pre-trained language models, and we believe that it can help further research in building robust NER systems.

null

28,778

inproceedings

zong-etal-2022-extracting

Extracting a Knowledge Base of {COVID}-19 Events from Social Media

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.335/

Zong, Shi and Baheti, Ashutosh and Xu, Wei and Ritter, Alan

Proceedings of the 29th International Conference on Computational Linguistics

3810--3823

We present a manually annotated corpus of 10,000 tweets containing public reports of five COVID-19 events, including positive and negative tests, deaths, denied access to testing, claimed cures and preventions. We designed slot-filling questions for each event type and annotated a total of 28 fine-grained slots, such as the location of events, recent travel, and close contacts. We show that our corpus can support fine-tuning BERT-based classifiers to automatically extract publicly reported events, which can be further collected for building a knowledge base. Our knowledge base is constructed over Twitter data covering two years and currently covers over 4.2M events. It can answer complex queries with high precision, such as {\textquotedblleft}Which organizations have employees that tested positive in Philadelphia?{\textquotedblright} We believe our proposed methodology could be quickly applied to develop knowledge bases for new domains in response to an emerging crisis, including natural disasters or future disease outbreaks.

null

28,779

inproceedings

wein-schneider-2022-accounting

Accounting for Language Effect in the Evaluation of Cross-lingual {AMR} Parsers

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.336/

Wein, Shira and Schneider, Nathan

Proceedings of the 29th International Conference on Computational Linguistics

3824--3834

Cross-lingual Abstract Meaning Representation (AMR) parsers are currently evaluated in comparison to gold English AMRs, despite parsing a language other than English, due to the lack of multilingual AMR evaluation metrics. This evaluation practice is problematic because of the established effect of source language on AMR structure. In this work, we present three multilingual adaptations of monolingual AMR evaluation metrics and compare the performance of these metrics to sentence-level human judgments. We then use our most highly correlated metric to evaluate the output of state-of-the-art cross-lingual AMR parsers, finding that Smatch may still be a useful metric in comparison to gold English AMRs, while our multilingual adaptation of S2match (XS2match) is best for comparison with gold in-language AMRs.

null

28,780

inproceedings

gollapalli-ng-2022-qsts

{QSTS}: A Question-Sensitive Text Similarity Measure for Question Generation

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.337/

Gollapalli, Sujatha Das and Ng, See-Kiong

Proceedings of the 29th International Conference on Computational Linguistics

3835--3846

While question generation (QG) has received significant focus in conversation modeling and text generation research, the problems of comparing questions and evaluation of QG models have remained inadequately addressed. Indeed, QG models continue to be evaluated using traditional measures such as BLEU, METEOR, and ROUGE scores which were designed for other text generation problems. We propose QSTS, a novel Question-Sensitive Text Similarity measure for comparing two questions by characterizing their target intent based on question class, named-entity, and semantic similarity information from the two questions. We show that QSTS addresses several shortcomings of existing measures that depend on $n$-gram overlap scores and obtains superior results compared to traditional measures on publicly-available QG datasets. We also collect a novel dataset SimQG, for enabling question similarity research in QG contexts. SimQG contains questions generated by state-of-the-art QG models along with human judgements on their relevance with respect to the passage context they were generated for as well as when compared to the given reference question. Using SimQG, we showcase the key aspect of QSTS that differentiates it from all existing measures. QSTS is not only able to characterize similarity between two questions, but is also able to score questions with respect to passage contexts. Thus QSTS is, to our knowledge, the first metric that enables the measurement of QG performance in a reference-free manner.

null

28,781

inproceedings

cha-etal-2022-noun

Noun-{MWP}: Math Word Problems Meet Noun Answers

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.338/

Cha, Taehun and Jung, Jaeheun and Lee, Donghun

Proceedings of the 29th International Conference on Computational Linguistics

3847--3857

We introduce a new type of problems for math word problem (MWP) solvers, named Noun-MWPs, whose answer is a non-numerical string containing a noun from the problem text. We present a novel method to empower existing MWP solvers to handle Noun-MWPs, and apply the method on Expression-Pointer Transformer (EPT). Our model, N-EPT, solves Noun-MWPs significantly better than other models, and at the same time, solves conventional MWPs as well. Solving Noun-MWPs may lead to bridging MWP solvers and traditional question-answering NLP models.

null

28,782

inproceedings

huynh-etal-2022-vinli

{V}i{NLI}: A {V}ietnamese Corpus for Studies on Open-Domain Natural Language Inference

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.339/

Huynh, Tin Van and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy

Proceedings of the 29th International Conference on Computational Linguistics

3858--3872

Over a decade, the research field of computational linguistics has witnessed the growth of corpora and models for natural language inference (NLI) for rich-resource languages such as English and Chinese. A large-scale and high-quality corpus is necessary for studies on NLI for Vietnamese, which can be considered a low-resource language. In this paper, we introduce ViNLI (Vietnamese Natural Language Inference), an open-domain and high-quality corpus for evaluating Vietnamese NLI models, which is created and evaluated with a strict process of quality control. ViNLI comprises over 30,000 human-annotated premise-hypothesis sentence pairs extracted from more than 800 online news articles on 13 distinct topics. In this paper, we introduce the guidelines for corpus creation which take the specific characteristics of the Vietnamese language in expressing entailment and contradiction into account. To evaluate the challenging level of our corpus, we conduct experiments with state-of-the-art deep neural networks and pre-trained models on our dataset. The best system performance is still far from human performance (a 14.20{\%} gap in accuracy). The ViNLI corpus is a challenging corpus to accelerate progress in Vietnamese computational linguistics. Our corpus is available publicly for research purposes.

null

28,783

inproceedings

kovatchev-taule-2022-inferes

{I}nfer{ES} : A Natural Language Inference Corpus for {S}panish Featuring Negation-Based Contrastive and Adversarial Examples

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.340/

Kovatchev, Venelin and Taul{\'e}, Mariona

Proceedings of the 29th International Conference on Computational Linguistics

3873--3884

In this paper we present InferES - an original corpus for Natural Language Inference (NLI) in European Spanish. We propose, implement, and analyze a variety of corpus-creating strategies utilizing expert linguists and crowd workers. The objectives behind InferES are to provide high-quality data, and at the same time to facilitate the systematic evaluation of automated systems. Specifically, we focus on measuring and improving the performance of machine learning systems on negation-based adversarial examples and their ability to generalize across out-of-distribution topics. We train two transformer models on InferES (8,055 gold examples) in a variety of scenarios. Our best model obtains 72.8{\%} accuracy, leaving a lot of room for improvement. The {\textquotedblleft}hypothesis-only{\textquotedblright} baseline performs only 2{\%}-5{\%} higher than majority, indicating much fewer annotation artifacts than prior work. We show that models trained on InferES generalize very well across topics (both in- and out-of-distribution) and perform moderately well on negation-based adversarial examples.

null

28,784

inproceedings

hao-etal-2022-parazh

{P}ara{Z}h-22{M}: A Large-Scale {C}hinese Parabank via Machine Translation

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.341/

Hao, Wenjie and Xu, Hongfei and Xiong, Deyi and Zan, Hongying and Mu, Lingling

Proceedings of the 29th International Conference on Computational Linguistics

3885--3897

Paraphrasing, i.e., restating the same meaning in different ways, is an important data augmentation approach for natural language processing (NLP). Zhang et al. (2019b) propose to extract sentence-level paraphrases from multiple Chinese translations of the same source texts, and construct the PKU Paraphrase Bank of 0.5M sentence pairs. However, despite being the largest Chinese parabank to date, the size of PKU parabank is limited by the availability of one-to-many sentence translation data, and cannot well support the training of large Chinese paraphrasers. In this paper, we relieve the restriction with one-to-many sentence translation data, and construct ParaZh-22M, a larger Chinese parabank that is composed of 22M sentence pairs, based on one-to-one bilingual sentence translation data and machine translation (MT). In our data augmentation experiments, we show that paraphrasing based on ParaZh-22M can bring about consistent and significant improvements over several strong baselines on a wide range of Chinese NLP tasks, including a number of Chinese natural language understanding benchmarks (CLUE) and low-resource machine translation.

null

28,785

inproceedings

wu-etal-2022-esimcse

{ES}im{CSE}: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.342/

Wu, Xing and Gao, Chaochen and Zang, Liangjun and Han, Jizhong and Wang, Zhongyuan and Hu, Songlin

Proceedings of the 29th International Conference on Computational Linguistics

3898--3907

Contrastive learning has been attracting much attention for learning unsupervised sentence embeddings. The current state-of-the-art unsupervised method is the unsupervised SimCSE (unsup-SimCSE). Unsup-SimCSE takes dropout as a minimal data augmentation method, and passes the same input sentence to a pre-trained Transformer encoder (with dropout turned on) twice to obtain the two corresponding embeddings to build a positive pair. As the length information of a sentence will generally be encoded into the sentence embeddings due to the usage of position embedding in Transformer, each positive pair in unsup-SimCSE actually contains the same length information. And thus unsup-SimCSE trained with these positive pairs is probably biased, which would tend to consider that sentences of the same or similar length are more similar in semantics. Through statistical observations, we find that unsup-SimCSE does have such a problem. To alleviate it, we apply a simple repetition operation to modify the input sentence, and then pass the input sentence and its modified counterpart to the pre-trained Transformer encoder, respectively, to get the positive pair. Additionally, we draw inspiration from the community of computer vision and introduce a momentum contrast, enlarging the number of negative pairs without additional calculations. The proposed two modifications are applied on positive and negative pairs separately, and build a new sentence embedding method, termed Enhanced Unsup-SimCSE (ESimCSE). We evaluate the proposed ESimCSE on several benchmark datasets w.r.t the semantic text similarity (STS) task. Experimental results show that ESimCSE outperforms the state-of-the-art unsup-SimCSE by an average Spearman correlation of 2.02{\%} on BERT-base.

null

28,786

inproceedings

yu-etal-2022-measuring

Measuring Robustness for {NLP}

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.343/

Yu, Yu and Khan, Abdul Rafae and Xu, Jia

Proceedings of the 29th International Conference on Computational Linguistics

3908--3916

The quality of Natural Language Processing (NLP) models is typically measured by the accuracy or error rate of a predefined test set. Because the evaluation and optimization of these measures are narrowed down to a specific domain like news and cannot be generalized to other domains like Twitter, we often observe that a system reported with human parity results generates surprising errors in real-life use scenarios. We address this weakness with a new approach that uses an NLP quality measure based on robustness. Unlike previous work that has defined robustness using Minimax to bound worst cases, we measure robustness based on the consistency of cross-domain accuracy and introduce the coefficient of variation and (epsilon, gamma)-Robustness. Our measures demonstrate higher agreements with human evaluation than accuracy scores like BLEU on ranking Machine Translation (MT) systems. Our experiments of sentiment analysis and MT tasks show that incorporating our robustness measures into learning objectives significantly enhances the final NLP prediction accuracy over various domains, such as biomedical and social media.

null

28,787

inproceedings

li-etal-2022-csl

{CSL}: A Large-scale {C}hinese Scientific Literature Dataset

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.344/

Li, Yudong and Zhang, Yuqing and Zhao, Zhe and Shen, Linlin and Liu, Weijie and Mao, Weiquan and Zhang, Hui

Proceedings of the 29th International Conference on Computational Linguistics

3917--3923

Scientific literature serves as a high-quality corpus, supporting a lot of Natural Language Processing (NLP) research. However, existing datasets are centered around the English language, which restricts the development of Chinese scientific NLP. In this work, we present CSL, a large-scale Chinese Scientific Literature dataset, which contains the titles, abstracts, keywords and academic fields of 396k papers. To our knowledge, CSL is the first scientific document dataset in Chinese. The CSL can serve as a Chinese corpus. Also, this semi-structured data is a natural annotation that can constitute many supervised NLP tasks. Based on CSL, we present a benchmark to evaluate the performance of models across scientific domain tasks, i.e., summarization, keyword generation and text classification. We analyze the behavior of existing text-to-text models on the evaluation tasks and reveal the challenges for Chinese scientific NLP tasks, which provides a valuable reference for future research. Data and code will be publicly available.

null

28,788

inproceedings

liu-etal-2022-singlish

{S}inglish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.345/

Liu, Zhengyuan and Ni, Shikang and Aw, Ai Ti and Chen, Nancy F.

Proceedings of the 29th International Conference on Computational Linguistics

3924--3936

Within the natural language processing community, English is by far the most resource-rich language. There is emerging interest in conducting translation via computational approaches to conform its dialects or creole languages back to standard English. This computational approach paves the way to leverage generic English language backbones, which are beneficial for various downstream tasks. However, in practical online communication scenarios, the use of language varieties is often accompanied by noisy user-generated content, making this translation task more challenging. In this work, we introduce a joint paraphrasing task of creole translation and text normalization of Singlish messages, which can shed light on how to process other language varieties and dialects. We formulate the task in three different linguistic dimensions: lexical level normalization, syntactic level editing, and semantic level rewriting. We build an annotated dataset of Singlish-to-Standard English messages, and report performance on a perturbation-resilient sequence-to-sequence model. Experimental results show that the model produces reasonable generation results, and can improve the performance of downstream tasks like stance detection.

null

28,789

inproceedings

yang-etal-2022-cino

{CINO}: A {C}hinese Minority Pre-trained Language Model

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.346/

Yang, Ziqing and Xu, Zihang and Cui, Yiming and Wang, Baoxin and Lin, Min and Wu, Dayong and Chen, Zhigang

Proceedings of the 29th International Conference on Computational Linguistics

3937--3949

Multilingual pre-trained language models have shown impressive performance on cross-lingual tasks. It greatly facilitates the applications of natural language processing on low-resource languages. However, there are still some languages that the current multilingual models do not perform well on. In this paper, we propose CINO (Chinese Minority Pre-trained Language Model), a multilingual pre-trained language model for Chinese minority languages. It covers Standard Chinese, Yue Chinese, and six other ethnic minority languages. To evaluate the cross-lingual ability of the multilingual model on ethnic minority languages, we collect documents from Wikipedia and news websites, and construct two text classification datasets, WCM (Wiki-Chinese-Minority) and CMNews (Chinese-Minority-News). We show that CINO notably outperforms the baselines on various classification tasks. The CINO model and the datasets are publicly available at \url{http://cino.hfl-rc.com}.

null

28,790

inproceedings

gari-soler-etal-2022-one

One Word, Two Sides: Traces of Stance in Contextualized Word Representations

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.347/

Gar{\'i} Soler, Aina and Labeau, Matthieu and Clavel, Chlo{\'e}

Proceedings of the 29th International Conference on Computational Linguistics

3950--3959

The way we use words is influenced by our opinion. We investigate whether this is reflected in contextualized word embeddings. For example, is the representation of {\textquotedblleft}animal{\textquotedblright} different between people who would abolish zoos and those who would not? We explore this question from a Lexical Semantic Change standpoint. Our experiments with BERT embeddings derived from datasets with stance annotations reveal small but significant differences in word representations between opposing stances.

null

28,791

inproceedings

leczkowski-etal-2022-prepositions

Prepositions Matter in Quantifier Scope Disambiguation

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.348/

Leczkowski, Aleksander and Grudzi{\'n}ska, Justyna and Vargas Guzm{\'a}n, Manuel and Wawer, Aleksander and Siemieniuk, Aleksandra

Proceedings of the 29th International Conference on Computational Linguistics

3960--3970

Although it is widely agreed that world knowledge plays a significant role in quantifier scope disambiguation (QSD), there has been only very limited work on how to integrate this knowledge into a QSD model. This paper contributes to this scarce line of research by incorporating into a machine learning model our knowledge about relations, as conveyed by a manageable closed class of function words: prepositions. For data, we use a scope-disambiguated corpus created by AnderBois, Brasoveanu and Henderson, which is additionally annotated with prepositional senses using Schneider et al`s Semantic Network of Adposition and Case Supersenses (SNACS) scheme. By applying Manshadi and Allen`s method to the corpus, we were able to inspect the information gain provided by prepositions for the QSD task. Statistical analysis of the performance of the classifiers, trained in scenarios with and without preposition information, supports the claim that prepositional senses have a strong positive impact on the learnability of automatic QSD systems.

null

28,792

inproceedings

gajbhiye-etal-2022-modelling

Modelling Commonsense Properties Using Pre-Trained Bi-Encoders

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.349/

Gajbhiye, Amit and Espinosa-Anke, Luis and Schockaert, Steven

Proceedings of the 29th International Conference on Computational Linguistics

3971--3983

Grasping the commonsense properties of everyday concepts is an important prerequisite to language understanding. While contextualised language models are reportedly capable of predicting such commonsense properties with human-level accuracy, we argue that such results have been inflated because of the high similarity between training and test concepts. This means that models which capture concept similarity can perform well, even if they do not capture any knowledge of the commonsense properties themselves. In settings where there is no overlap between the properties that are considered during training and testing, we find that the empirical performance of standard language models drops dramatically. To address this, we study the possibility of fine-tuning language models to explicitly model concepts and their properties. In particular, we train separate concept and property encoders on two types of readily available data: extracted hyponym-hypernym pairs and generic sentences. Our experimental results show that the resulting encoders allow us to predict commonsense properties with much higher accuracy than is possible by directly fine-tuning language models. We also present experimental results for the related task of unsupervised hypernym discovery.

null

28,793

inproceedings

schneider-etal-2022-coin

{COIN} {--} an Inexpensive and Strong Baseline for Predicting Out of Vocabulary Word Embeddings

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.350/

Schneider, Andrew and He, Lihong and Chen, Zhijia and Mukherjee, Arjun and Dragut, Eduard

Proceedings of the 29th International Conference on Computational Linguistics

3984--3993

Social media is the ultimate challenge for many natural language processing tools. The constant emergence of linguistic constructs challenge even the most sophisticated NLP tools. Predicting word embeddings for out of vocabulary words is one of those challenges. Word embedding models only include terms that occur a sufficient number of times in their training corpora. Word embedding vector models are unable to directly provide any useful information about a word not in their vocabularies. We propose a fast method for predicting vectors for out of vocabulary terms that makes use of the surrounding terms of the unknown term and the hidden context layer of the word2vec model. We propose this method as a strong baseline in the sense that 1) while it does not surpass all state-of-the-art methods, it surpasses several techniques for vector prediction on benchmark tasks, 2) even when it underperforms, the margin is very small retaining competitive performance in downstream tasks, and 3) it is inexpensive to compute, requiring no additional training stage. We also show that our technique can be incorporated into existing methods to achieve a new state-of-the-art on the word vector prediction problem.

null

28,794

inproceedings

li-etal-2022-dyngl

{D}yn{GL}-{SDP}: Dynamic Graph Learning for Semantic Dependency Parsing

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.351/

Li, Bin and Gao, Miao and Fan, Yunlong and Sataer, Yikemaiti and Gao, Zhiqiang and Gui, Yaocheng

Proceedings of the 29th International Conference on Computational Linguistics

3994--4004

A recent success in semantic dependency parsing shows that graph neural networks can make significant accuracy improvements, owing to its powerful ability in learning expressive graph representations. However, this work learns graph representations based on a static graph constructed by an existing parser, suffering from two drawbacks: (1) the static graph might be error-prone (e.g., noisy or incomplete), and (2) graph construction stage and graph representation learning stage are disjoint, the errors introduced in the graph construction stage cannot be corrected and might be accumulated to later stages. To address these two drawbacks, we propose a dynamic graph learning framework and apply it to semantic dependency parsing, for jointly learning graph structure and graph representations. Experimental results show that our parser outperforms the previous parsers on the SemEval-2015 Task 18 dataset in three languages (English, Chinese, and Czech).

null

28,795

inproceedings

chen-etal-2022-knowledge

Knowledge Is Flat: A {S}eq2{S}eq Generative Framework for Various Knowledge Graph Completion

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.352/

Chen, Chen and Wang, Yufei and Li, Bing and Lam, Kwok-Yan

Proceedings of the 29th International Conference on Computational Linguistics

4005--4017

Knowledge Graph Completion (KGC) has been recently extended to multiple knowledge graph (KG) structures, initiating new research directions, e.g. static KGC, temporal KGC and few-shot KGC. Previous works often design KGC models closely coupled with specific graph structures, which inevitably results in two drawbacks: 1) structure-specific KGC models are mutually incompatible; 2) existing KGC methods are not adaptable to emerging KGs. In this paper, we propose KG-S2S, a Seq2Seq generative framework that could tackle different verbalizable graph structures by unifying the representation of KG facts into {\textquotedblleft}flat{\textquotedblright} text, regardless of their original form. To remedy the KG structure information loss from the {\textquotedblleft}flat{\textquotedblright} text, we further improve the input representations of entities and relations, and the inference algorithm in KG-S2S. Experiments on five benchmarks show that KG-S2S outperforms many competitive baselines, setting new state-of-the-art performance. Finally, we analyze KG-S2S`s ability on the different relations and the Non-entity Generations.

null

28,796

inproceedings

chiarcos-etal-2022-modelling-frequency

Modelling Frequency, Attestation, and Corpus-Based Information with {O}nto{L}ex-{F}r{AC}

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.353/

Chiarcos, Christian and Apostol, Elena-Simona and Kabashi, Besim and Truic{\u{a}}, Ciprian-Octavian

Proceedings of the 29th International Conference on Computational Linguistics

4018--4027

OntoLex-Lemon has become a de facto standard for lexical resources in the web of data. This paper provides the first overall description of the emerging OntoLex module for Frequency, Attestations, and Corpus-Based Information (OntoLex-FrAC) that is intended to complement OntoLex-Lemon with the necessary vocabulary to represent major types of information found in or automatically derived from corpora, for applications in both language technology and the language sciences.

null

28,797

inproceedings

chen-palmer-2022-contrast

Contrast Sets for Stativity of {E}nglish Verbs in Context

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.354/

Chen, Daniel and Palmer, Alexis

Proceedings of the 29th International Conference on Computational Linguistics

4028--4036

For the task of classifying verbs in context as dynamic or stative, current models approach human performance, but only for particular data sets. To better understand the performance of such models, and how well they are able to generalize beyond particular test sets, we apply the contrast set (Gardner et al., 2020) methodology to stativity classification. We create nearly 300 contrastive pairs by perturbing test set instances just enough to change their labels from one class to the other, while preserving coherence, meaning, and well-formedness. Contrastive evaluation shows that a model with near-human performance on an in-distribution test set degrades substantially when applied to transformed examples, showing that the stative vs. dynamic classification task is more complex than the model performance might otherwise suggest. Code and data are freely available.

null

28,798

inproceedings

zosa-pivovarova-2022-multilingual

Multilingual and Multimodal Topic Modelling with Pretrained Embeddings

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.355/

Zosa, Elaine and Pivovarova, Lidia

Proceedings of the 29th International Conference on Computational Linguistics

4037--4048

This paper presents M3L-Contrast{---}a novel multimodal multilingual (M3L) neural topic model for comparable data that maps texts from multiple languages and images into a shared topic space. Our model is trained jointly on texts and images and takes advantage of pretrained document and image embeddings to abstract the complexities between different languages and modalities. As a multilingual topic model, it produces aligned language-specific topics and as multimodal model, it infers textual representations of semantic concepts in images. We demonstrate that our model is competitive with a zero-shot topic model in predicting topic distributions for comparable multilingual data and significantly outperforms a zero-shot model in predicting topic distributions for comparable texts and images. We also show that our model performs almost as well on unaligned embeddings as it does on aligned embeddings.

null

28,799

inproceedings

zhai-etal-2022-zero

Zero-shot Script Parsing

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.356/

Zhai, Fangzhou and Demberg, Vera and Koller, Alexander

Proceedings of the 29th International Conference on Computational Linguistics

4049--4060

Script knowledge is useful to a variety of NLP tasks. However, existing resources only cover a small number of activities, limiting its practical usefulness. In this work, we propose a zero-shot learning approach to \textbf{script parsing}, the task of tagging texts with scenario-specific event and participant types, which enables us to acquire script knowledge without domain-specific annotations. We (1) learn representations of potential event and participant mentions by promoting cluster consistency according to the annotated data; (2) perform clustering on the event / participant candidates from unannotated texts that belongs to an unseen scenario. The model achieves 68.1/74.4 average F1 for event / participant parsing, respectively, outperforming a previous CRF model that, in contrast, has access to scenario-specific supervision. We also evaluate the model by testing on a different corpus, where it achieved 55.5/54.0 average F1 for event / participant parsing.

null

28,800

inproceedings

zhang-etal-2022-word

Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.357/

Zhang, Guobiao and Lu, Wenpeng and Peng, Xueping and Wang, Shoujin and Kan, Baoshuo and Yu, Rui

Proceedings of the 29th International Conference on Computational Linguistics

4061--4070

Word sense disambiguation (WSD), identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory, is one of the most classical and challenging tasks in natural language processing. Benefiting from the powerful ability of deep neural networks, WSD has achieved a great advancement in recent years. Reformulating WSD as a text span extraction task is an effective approach, which accepts a sentence context of an ambiguous word together with all definitions of its candidate senses simultaneously, and requires to extract the text span corresponding with the right sense. However, the approach merely depends on a short definition to learn sense representation, which neglects abundant semantic knowledge from related senses and leads to data-inefficient learning and suboptimal WSD performance. To address the limitations, we propose a novel WSD method with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension (KELESC). Specifically, a knowledge-enhanced method is proposed to enrich semantic representation by incorporating additional examples and definitions of the related senses in WordNet. Then, in order to avoid the huge computing complexity induced by the additional information, a local self-attention mechanism is utilized to constrain attention to be local, which allows longer input texts without large-scale computing burdens. Extensive experimental results demonstrate that KELESC achieves better performance than baseline models on public benchmark datasets.

null

28,801

inproceedings

sandhan-etal-2022-novel

A Novel Multi-Task Learning Approach for Context-Sensitive Compound Type Identification in {S}anskrit

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.358/

Sandhan, Jivnesh and Gupta, Ashish and Terdalkar, Hrishikesh and Sandhan, Tushar and Samanta, Suvendu and Behera, Laxmidhar and Goyal, Pawan

Proceedings of the 29th International Conference on Computational Linguistics

4071--4083

The phenomenon of compounding is ubiquitous in Sanskrit. It serves for achieving brevity in expressing thoughts, while simultaneously enriching the lexical and structural formation of the language. In this work, we focus on the Sanskrit Compound Type Identification (SaCTI) task, where we consider the problem of identifying semantic relations between the components of a compound word. Earlier approaches solely rely on the lexical information obtained from the components and ignore the most crucial contextual and syntactic information useful for SaCTI. However, the SaCTI task is challenging primarily due to the implicitly encoded context-sensitive semantic relation between the compound components. Thus, we propose a novel multi-task learning architecture which incorporates the contextual information and enriches the complementary syntactic information using morphological tagging and dependency parsing as two auxiliary tasks. Experiments on the benchmark datasets for SaCTI show 6.1 points (Accuracy) and 7.7 points (F1-score) absolute gain compared to the state-of-the-art system. Further, our multi-lingual experiments demonstrate the efficacy of the proposed architecture in English and Marathi languages.

null

28,802

inproceedings

bertolini-etal-2022-testing

Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.359/

Bertolini, Lorenzo and Weeds, Julie and Weir, David

Proceedings of the 29th International Conference on Computational Linguistics

4084--4100

Previous work has demonstrated that pre-trained large language models (LLM) acquire knowledge during pre-training which enables reasoning over relationships between words (e.g, hyponymy) and more complex inferences over larger units of meaning such as sentences. Here, we investigate whether lexical entailment (LE, i.e. hyponymy or the is a relation between words) can be generalised in a compositional manner. Accordingly, we introduce PLANE (Phrase-Level Adjective-Noun Entailment), a new benchmark to test models on fine-grained compositional entailment using adjective-noun phrases. Our experiments show that knowledge extracted via In{--}Context and transfer learning is not enough to solve PLANE. However, a LLM trained on PLANE can generalise well to out{--}of{--}distribution sets, since the required knowledge can be stored in the representations of subwords (SW) tokens.

null

28,803

inproceedings

proietti-etal-2022-bert

Does {BERT} Recognize an Agent? Modeling {D}owty`s Proto-Roles with Contextual Embeddings

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.360/

Proietti, Mattia and Lebani, Gianluca and Lenci, Alessandro

Proceedings of the 29th International Conference on Computational Linguistics

4101--4112

Contextual embeddings build multidimensional representations of word tokens based on their context of occurrence. Such models have been shown to achieve a state-of-the-art performance on a wide variety of tasks. Yet, the community struggles in understanding what kind of semantic knowledge these representations encode. We report a series of experiments aimed at investigating to what extent one of such models, BERT, is able to infer the semantic relations that, according to Dowty`s Proto-Roles theory, a verbal argument receives by virtue of its role in the event described by the verb. This hypothesis were put to test by learning a linear mapping from the BERT`s verb embeddings to an interpretable space of semantic properties built from the linguistic dataset by White et al. (2016). In a first experiment we tested whether the semantic properties inferred from a typed version of the BERT embeddings would be more linguistically plausible than those produced by relying on static embeddings. We then move to evaluate the semantic properties inferred from the contextual embeddings both against those available in the original dataset, as well as by assessing their ability to model the semantic properties possessed by the agent of the verbs participating in the so-called causative alternation.

null

28,804

inproceedings

peng-etal-2022-towards

Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.361/

Peng, Qiwei and Weir, David and Weeds, Julie

Proceedings of the 29th International Conference on Computational Linguistics

4113--4123

Previous works have demonstrated the effectiveness of utilising pre-trained sentence encoders based on their sentence representations for meaning comparison tasks. Though such representations are shown to capture hidden syntax structures, the direct similarity comparison between them exhibits weak sensitivity to word order and structural differences in given sentences. A single similarity score further makes the comparison process hard to interpret. Therefore, we here propose to combine sentence encoders with an alignment component by representing each sentence as a list of predicate-argument spans (where their span representations are derived from sentence encoders), and decomposing the sentence-level meaning comparison into the alignment between their spans for paraphrase identification tasks. Empirical results show that the alignment component brings in both improved performance and interpretability for various sentence encoders. After closer investigation, the proposed approach indicates increased sensitivity to structural difference and enhanced ability to distinguish non-paraphrases with high lexical overlap.

null

28,805

inproceedings

seneviratne-etal-2022-cilex

{CIL}ex: An Investigation of Context Information for Lexical Substitution Methods

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.362/

Seneviratne, Sandaru and Daskalaki, Elena and Lenskiy, Artem and Suominen, Hanna

Proceedings of the 29th International Conference on Computational Linguistics

4124--4135

Lexical substitution, which aims to generate substitutes for a target word given a context, is an important natural language processing task useful in many applications. Due to the paucity of annotated data, existing methods for lexical substitution tend to rely on manually curated lexical resources and contextual word embedding models. Methods based on lexical resources are likely to miss relevant substitutes whereas relying only on contextual word embedding models fails to provide adequate information on the impact of a substitute in the entire context and the overall meaning of the input. We proposed CILex, which uses contextual sentence embeddings along with methods that capture additional context information complimenting contextual word embeddings for lexical substitution. This ensured the semantic consistency of a substitute with the target word while maintaining the overall meaning of the sentence. Our experimental comparisons with previously proposed methods indicated that our solution is now the state-of-the-art on both the widely used LS07 and CoInCo datasets with P@1 scores of 55.96{\%} and 57.25{\%} for lexical substitution. The implementation of the proposed approach is available at \url{https://github.com/sandaruSen/CILex} under the MIT license.

null

28,806

inproceedings

shah-etal-2022-emotion

Emotion Enriched Retrofitted Word Embeddings

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.363/

Shah, Sapan and Reddy, Sreedhar and Bhattacharyya, Pushpak

Proceedings of the 29th International Conference on Computational Linguistics

4136--4148

Word embeddings learned using the distributional hypothesis (e.g., GloVe, Word2vec) are good at encoding various lexical-semantic relations. However, they do not capture the emotion aspects of words. We present a novel retrofitting method for updating the vectors of emotion bearing words like fun, offence, angry, etc. The retrofitted embeddings achieve better inter-cluster and intra-cluster distance for words having the same emotions, e.g., the joy cluster containing words like fun, happiness, etc., and the anger cluster with words like offence, rage, etc., as evaluated through different cluster quality metrics. For the downstream tasks on sentiment analysis and sarcasm detection, simple classification models, such as SVM and Attention Net, learned using our retrofitted embeddings perform better than their pre-trained counterparts (about 1.5 {\%} improvement in F1-score) as well as other benchmarks. Furthermore, the difference in performance is more pronounced in the limited data setting.

null

28,807

inproceedings

zhang-liu-2022-metaphor

Metaphor Detection via Linguistics Enhanced {S}iamese Network

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.364/

Zhang, Shenglong and Liu, Ying

Proceedings of the 29th International Conference on Computational Linguistics

4149--4159

In this paper we present MisNet, a novel model for word level metaphor detection. MisNet converts two linguistic rules, i.e., Metaphor Identification Procedure (MIP) and Selectional Preference Violation (SPV) into semantic matching tasks. MIP module computes the similarity between the contextual meaning and the basic meaning of a target word. SPV module perceives the incongruity between target words and their contexts. To better represent basic meanings, MisNet utilizes dictionary resources. Empirical results indicate that MisNet achieves competitive performance on several datasets.

null

28,808

inproceedings

zhou-etal-2022-fast

Fast and Accurate End-to-End Span-based Semantic Role Labeling as Word-based Graph Parsing

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.365/

Zhou, Shilin and Xia, Qingrong and Li, Zhenghua and Zhang, Yu and Hong, Yu and Zhang, Min

Proceedings of the 29th International Conference on Computational Linguistics

4160--4171

This paper proposes to cast end-to-end span-based SRL as a word-based graph parsing task. The major challenge is how to represent spans at the word level. Borrowing ideas from research on Chinese word segmentation and named entity recognition, we propose and compare four different schemata of graph representation, i.e., BES, BE, BIES, and BII, among which we find that the BES schema performs the best. We further gain interesting insights through detailed analysis. Moreover, we propose a simple constrained Viterbi procedure to ensure the legality of the output graph according to the constraints of the SRL structure. We conduct experiments on two widely used benchmark datasets, i.e., CoNLL05 and CoNLL12. Results show that our word-based graph parsing approach achieves consistently better performance than previous results, under all settings of end-to-end and predicate-given, without and with pre-trained language models (PLMs). More importantly, our model can parse 669/252 sentences per second, without and with PLMs respectively.

null

28,809

inproceedings

wada-etal-2022-unsupervised

Unsupervised Lexical Substitution with Decontextualised Embeddings

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.366/

Wada, Takashi and Baldwin, Timothy and Matsumoto, Yuji and Lau, Jey Han

Proceedings of the 29th International Conference on Computational Linguistics

4172--4185

We propose a new unsupervised method for lexical substitution using pre-trained language models. Compared to previous approaches that use the generative capability of language models to predict substitutes, our method retrieves substitutes based on the similarity of contextualised and decontextualised word embeddings, i.e. the average contextual representation of a word in multiple contexts. We conduct experiments in English and Italian, and show that our method substantially outperforms strong baselines and establishes a new state-of-the-art without any explicit supervision or fine-tuning. We further show that our method performs particularly well at predicting low-frequency substitutes, and also generates a diverse list of substitute candidates, reducing morphophonetic or morphosyntactic biases induced by article-noun agreement.

null

28,810

inproceedings

poelman-etal-2022-transparent

Transparent Semantic Parsing with {U}niversal {D}ependencies Using Graph Transformations

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.367/

Poelman, Wessel and van Noord, Rik and Bos, Johan

Proceedings of the 29th International Conference on Computational Linguistics

4186--4192

Even though many recent semantic parsers are based on deep learning methods, we should not forget that rule-based alternatives might offer advantages over neural approaches with respect to transparency, portability, and explainability. Taking advantage of existing off-the-shelf Universal Dependency parsers, we present a method that maps a syntactic dependency tree to a formal meaning representation based on Discourse Representation Theory. Rather than using lambda calculus to manage variable bindings, our approach is novel in that it consists of using a series of graph transformations. The resulting UD semantic parser shows good performance for English, German, Italian and Dutch, with F-scores over 75{\%}, outperforming a neural semantic parser for the lower-resourced languages. Unlike neural semantic parsers, our UD semantic parser does not hallucinate output, is relatively easy to port to other languages, and is completely transparent.

null

28,811

inproceedings

su-etal-2022-multilingual

Multilingual Word Sense Disambiguation with Unified Sense Representation

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.368/

Su, Ying and Zhang, Hongming and Song, Yangqiu and Zhang, Tong

Proceedings of the 29th International Conference on Computational Linguistics

4193--4202

As a key natural language processing (NLP) task, word sense disambiguation (WSD) evaluates how well NLP models can understand the fine-grained semantics of words under specific contexts. Benefited from the large-scale annotation, current WSD systems have achieved impressive performances in English by combining supervised learning with lexical knowledge. However, such success is hard to be replicated in other languages, where we only have very limited annotations. In this paper, based on that the multilingual lexicon BabelNet describing the same set of concepts across languages, we propose to build knowledge and supervised based Multilingual Word Sense Disambiguation (MWSD) systems. We build unified sense representations for multiple languages and address the annotation scarcity problem for MWSD by transferring annotations from rich sourced languages. With the unified sense representations, annotations from multiple languages can be jointly trained to benefit the MWSD tasks. Evaluations of SemEval-13 and SemEval-15 datasets demonstrate the effectiveness of our methodology.

null

28,812

inproceedings

xia-etal-2022-transition

A Transition-based Method for Complex Question Understanding

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.369/

Xia, Yu and Jiang, Wenbin and Lyu, Yajuan and Li, Sujian

Proceedings of the 29th International Conference on Computational Linguistics

4203--4211

Complex Question Understanding (CQU) parses complex questions to Question Decomposition Meaning Representation (QDMR) which is a sequence of atomic operators. Existing works are based on end-to-end neural models which do not explicitly model the intermediate states and lack interpretability for the parsing process. Besides, they predict QDMR in a mismatched granularity and do not model the step-wise information which is an essential characteristic of QDMR. To alleviate the issues, we treat QDMR as a computational graph and propose a transition-based method where a \textit{decider} predicts a sequence of actions to build the graph node-by-node. In this way, the partial graph at each step enables better representation of the intermediate states and better interpretability. At each step, the decider encodes the intermediate state with specially designed encoders and predicts several candidates of the next action and its confidence. For inference, a searcher seeks the optimal graph based on the predictions of the decider to alleviate the error propagation. Experimental results demonstrate the parsing accuracy of our method against several strong baselines. Moreover, our method has transparent and human-readable intermediate results, showing improved interpretability.

null

28,813

inproceedings

zhang-etal-2022-semantic

Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures inside Arguments

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.370/

Zhang, Yu and Xia, Qingrong and Zhou, Shilin and Jiang, Yong and Fu, Guohong and Zhang, Min

Proceedings of the 29th International Conference on Computational Linguistics

4212--4227

Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community. Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based. Despite ubiquity, they share some intrinsic drawbacks of not considering internal argument structures, potentially hindering the model`s expressiveness. The key challenge is arguments are flat structures, and there are no determined subtree realizations for words inside arguments. To remedy this, in this paper, we propose to regard flat argument spans as latent subtrees, accordingly reducing SRL to a tree parsing task. In particular, we equip our formulation with a novel span-constrained TreeCRF to make tree structures span-aware and further extend it to the second-order case. We conduct extensive experiments on CoNLL05 and CoNLL12 benchmarks. Results reveal that our methods perform favorably better than all previous syntax-agnostic works, achieving new state-of-the-art under both end-to-end and w/ gold predicates settings.

null

28,814

inproceedings

wang-etal-2022-noisy

Noisy Label Regularisation for Textual Regression

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.371/

Wang, Yuxia and Baldwin, Timothy and Verspoor, Karin

Proceedings of the 29th International Conference on Computational Linguistics

4228--4240

Training with noisy labelled data is known to be detrimental to model performance, especially for high-capacity neural network models in low-resource domains. Our experiments suggest that standard regularisation strategies, such as weight decay and dropout, are ineffective in the face of noisy labels. We propose a simple noisy label detection method that prevents error propagation from the input layer. The approach is based on the observation that the projection of noisy labels is learned through memorisation at advanced stages of learning, and that the Pearson correlation is sensitive to outliers. Extensive experiments over real-world human-disagreement annotations as well as randomly-corrupted and data-augmented labels, across various tasks and domains, demonstrate that our method is effective, regularising noisy labels and improving generalisation performance.

null

28,815

inproceedings

bialer-etal-2022-detecting

Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.372/

Bialer, Amir and Izmaylov, Daniel and Segal, Avi and Tsur, Oren and Levi-Belz, Yossi and Gal, Kobi

Proceedings of the 29th International Conference on Computational Linguistics

4241--4250

With the increased awareness of situations of mental crisis and their societal impact, online services providing emergency support are becoming commonplace in many countries. Computational models, trained on discussions between help-seekers and providers, can support suicide prevention by identifying at-risk individuals. However, the lack of domain-specific models, especially in low-resource languages, poses a significant challenge for the automatic detection of suicide risk. We propose a model that combines pre-trained language models (PLM) with a fixed set of manually crafted (and clinically approved) set of suicidal cues, followed by a two-stage fine-tuning process. Our model achieves 0.91 ROC-AUC and an F2-score of 0.55, significantly outperforming an array of strong baselines even early on in the conversation, which is critical for real-time detection in the field. Moreover, the model performs well across genders and age groups.

null

28,816

inproceedings

roy-etal-2022-meta

Does Meta-learning Help m{BERT} for Few-shot Question Generation in a Cross-lingual Transfer Setting for Indic Languages?

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.373/

Roy, Aniruddha and Thakur, Rupak Kumar and Sharma, Isha and Gupta, Ashim and Krishna, Amrith and Sarkar, Sudeshna and Goyal, Pawan

Proceedings of the 29th International Conference on Computational Linguistics

4251--4257

Few-shot Question Generation (QG) is an important and challenging problem in the Natural Language Generation (NLG) domain. Multilingual BERT (mBERT) has been successfully used in various Natural Language Understanding (NLU) applications. However, the question of how to utilize mBERT for few-shot QG, possibly with cross-lingual transfer, remains. In this paper, we try to explore how mBERT performs in few-shot QG (cross-lingual transfer) and also whether applying meta-learning on mBERT further improves the results. In our setting, we consider mBERT as the base model and fine-tune it using a seq-to-seq language modeling framework in a cross-lingual setting. Further, we apply the model agnostic meta-learning approach to our base model. We evaluate our model for two low-resource Indian languages, Bengali and Telugu, using the TyDi QA dataset. The proposed approach consistently improves the performance of the base model in few-shot settings and even works better than some heavily parameterized models. Human evaluation also confirms the effectiveness of our approach.

null

28,817

inproceedings

oncevay-etal-2022-revisiting

Revisiting Syllables in Language Modelling and Their Application on Low-Resource Machine Translation

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.374/

Oncevay, Arturo and Rivas Rojas, Kervy Dante and Chavez Sanchez, Liz Karen and Zariquiey, Roberto

Proceedings of the 29th International Conference on Computational Linguistics

4258--4267

Language modelling and machine translation tasks mostly use subword or character inputs, but syllables are seldom used. Syllables provide shorter sequences than characters, require less-specialised extracting rules than morphemes, and their segmentation is not impacted by the corpus size. In this study, we first explore the potential of syllables for open-vocabulary language modelling in 21 languages. We use rule-based syllabification methods for six languages and address the rest with hyphenation, which works as a syllabification proxy. With a comparable perplexity, we show that syllables outperform characters and other subwords. Moreover, we study the importance of syllables on neural machine translation for a non-related and low-resource language-pair (Spanish{--}Shipibo-Konibo). In pairwise and multilingual systems, syllables outperform unsupervised subwords, and further morphological segmentation methods, when translating into a highly synthetic language with a transparent orthography (Shipibo-Konibo). Finally, we perform some human evaluation, and discuss limitations and opportunities.

null

28,818

inproceedings

fazili-jyothi-2022-aligning

Aligning Multilingual Embeddings for Improved Code-switched Natural Language Understanding

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.375/

Fazili, Barah and Jyothi, Preethi

Proceedings of the 29th International Conference on Computational Linguistics

4268--4273

Multilingual pretrained models, while effective on monolingual data, need additional training to work well with code-switched text. In this work, we present a novel idea of training multilingual models with alignment objectives using parallel text so as to explicitly align word representations with the same underlying semantics across languages. Such an explicit alignment step has a positive downstream effect and improves performance on multiple code-switched NLP tasks. We explore two alignment strategies and report improvements of up to 7.32{\%}, 0.76{\%} and 1.9{\%} on Hindi-English Sentiment Analysis, Named Entity Recognition and Question Answering tasks compared to a competitive baseline model.

null

28,819

inproceedings

le-ferrand-etal-2022-fashioning

Fashioning Local Designs from Generic Speech Technologies in an {A}ustralian Aboriginal Community

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.376/

Le Ferrand, {\'E}ric and Bird, Steven and Besacier, Laurent

Proceedings of the 29th International Conference on Computational Linguistics

4274--4285

An increasing number of papers have been addressing issues related to low-resource languages and the transcription bottleneck paradigm. After several years spent in Northern Australia, where some of the strongest Aboriginal languages are spoken, we could observe a gap between the motivations depicted in research contributions in this space and the Northern Australian context. In this paper, we address this gap in research by exploring the potential of speech recognition in an Aboriginal community. We describe our work from training a spoken term detection system to its implementation in an activity with Aboriginal participants. We report here on one side how speech recognition technologies can find their place in an Aboriginal context and, on the other, methodological paths that allowed us to reach better comprehension and engagement from Aboriginal participants.

null

28,820

inproceedings

chang-etal-2022-shot

Few-Shot Pidgin Text Adaptation via Contrastive Fine-Tuning

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.377/

Chang, Ernie and Alabi, Jesujoba O. and Adelani, David Ifeoluwa and Demberg, Vera

Proceedings of the 29th International Conference on Computational Linguistics

4286--4291

The surging demand for multilingual dialogue systems often requires a costly labeling process for each language addition. For low resource languages, human annotators are continuously tasked with the adaptation of resource-rich language utterances for each new domain. However, this prohibitive and impractical process can often be a bottleneck for low resource languages that are still without proper translation systems nor parallel corpus. In particular, it is difficult to obtain task-specific low resource language annotations for the English-derived creoles (e.g. Nigerian and Cameroonian Pidgin). To address this issue, we utilize the pretrained language models i.e. BART which has shown great potential in language generation/understanding {--} we propose to finetune the BART model to generate utterances in Pidgin by leveraging the proximity of the source and target languages, and utilizing positive and negative examples in constrastive training objectives. We collected and released the first parallel Pidgin-English conversation corpus in two dialogue domains and showed that this simple and effective technique is suffice to yield impressive results for English-to-Pidgin generation, which are two closely-related languages.

null

28,821

inproceedings

nicolai-etal-2022-penalizing

Penalizing Divergence: Multi-Parallel Translation for Low-Resource Languages of {N}orth {A}merica

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.378/

Nicolai, Garrett and Yang, Changbing and Silfverberg, Miikka

Proceedings of the 29th International Conference on Computational Linguistics

4292--4298

This paper explores a special case in multilingual machine translation: so called multi-parallel translation, where the target data for all language pairs are identical. While multi-parallelism offers benefits which are not available in a standard translation setting, translation models can easily overfit when training data are limited. We introduce a regularizer, the divergence penalty, which penalizes the translation model when it represents source sentences with identical target translations in divergent ways. Experiments on very low-resourced Indigenous North American languages show that an initially deficient multilingual translator can improve by 4.9 BLEU through mBART pre-training, and 5.5 BLEU points with the strategic addition of monolingual data, and that a divergence penalty leads to further increases of 0.4 BLEU. Further experiments on Germanic languages demonstrate a improvement of 0.5 BLEU when applying the divergence penalty. An investigation of the neural encoder representations learned by our translation models shows that the divergence penalty encourages models to learn a unified neural interlingua.

null

28,822

inproceedings

simons-etal-2022-assessing

Assessing Digital Language Support on a Global Scale

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.379/

Simons, Gary F. and Thomas, Abbey L. L. and White, Chad K. K.

Proceedings of the 29th International Conference on Computational Linguistics

4299--4305

The users of endangered languages struggle to thrive in a digitally-mediated world. We have developed an automated method for assessing how well every language recognized by ISO 639 is faring in terms of digital language support. The assessment is based on scraping the names of supported languages from the websites of 143 digital tools selected to represent a full range of ways that digital technology can support languages. The method uses Mokken scale analysis to produce an explainable model for quantifying digital language support and monitoring it on a global scale.

null

28,823

inproceedings

soudani-etal-2022-persian

{P}ersian Natural Language Inference: A Meta-learning Approach

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.380/

Soudani, Heydar and Mojab, Mohammad Hassan and Beigy, Hamid

Proceedings of the 29th International Conference on Computational Linguistics

4306--4319

Incorporating information from other languages can improve the results of tasks in low-resource languages. A powerful method of building functional natural language processing systems for low-resource languages is to combine multilingual pre-trained representations with cross-lingual transfer learning. In general, however, shared representations are learned separately, either across tasks or across languages. This paper proposes a meta-learning approach for inferring natural language in Persian. Alternately, meta-learning uses different task information (such as QA in Persian) or other language information (such as natural language inference in English). Also, we investigate the role of task augmentation strategy for forming additional high-quality tasks. We evaluate the proposed method using four languages and an auxiliary task. Compared to the baseline approach, the proposed model consistently outperforms it, improving accuracy by roughly six percent. We also examine the effect of finding appropriate initial parameters using zero-shot evaluation and CCA similarity.

null

28,824

inproceedings

mondal-etal-2022-global

Global Readiness of Language Technology for Healthcare: What Would It Take to Combat the Next Pandemic?

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.381/

Mondal, Ishani and Ahuja, Kabir and Jain, Mohit and O{'}Neill, Jacki and Bali, Kalika and Choudhury, Monojit

Proceedings of the 29th International Conference on Computational Linguistics

4320--4335

The COVID-19 pandemic has brought out both the best and worst of language technology (LT). On one hand, conversational agents for information dissemination and basic diagnosis have seen widespread use, and arguably, had an important role in fighting against the pandemic. On the other hand, it has also become clear that such technologies are readily available for a handful of languages, and the vast majority of the global south is completely bereft of these benefits. What is the state of LT, especially conversational agents, for healthcare across the world`s languages? And, what would it take to ensure global readiness of LT before the next pandemic? In this paper, we try to answer these questions through survey of existing literature and resources, as well as through a rapid chatbot building exercise for 15 Asian and African languages with varying amount of resource-availability. The study confirms the pitiful state of LT even for languages with large speaker bases, such as Sinhala and Hausa, and identifies the gaps that could help us prioritize research and investment strategies in LT for healthcare.

null

28,825

inproceedings

alabi-etal-2022-adapting

Adapting Pre-trained Language Models to {A}frican Languages via Multilingual Adaptive Fine-Tuning

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.382/

Alabi, Jesujoba O. and Adelani, David Ifeoluwa and Mosbach, Marius and Klakow, Dietrich

Proceedings of the 29th International Conference on Computational Linguistics

4336--4349

Multilingual pre-trained language models (PLMs) have demonstrated impressive performance on several downstream tasks for both high-resourced and low-resourced languages. However, there is still a large performance drop for languages unseen during pre-training, especially African languages. One of the most effective approaches to adapt to a new language is language adaptive fine-tuning (LAFT) {---} fine-tuning a multilingual PLM on monolingual texts of a language using the pre-training objective. However, adapting to target language individually takes large disk space and limits the cross-lingual transfer abilities of the resulting models because they have been specialized for a single language. In this paper, we perform multilingual adaptive fine-tuning on 17 most-resourced African languages and three other high-resource languages widely spoken on the African continent to encourage cross-lingual transfer learning. To further specialize the multilingual PLM, we removed vocabulary tokens from the embedding layer that corresponds to non-African writing scripts before MAFT, thus reducing the model size by around 50{\%}. Our evaluation on two multilingual PLMs (AfriBERTa and XLM-R) and three NLP tasks (NER, news topic classification, and sentiment classification) shows that our approach is competitive to applying LAFT on individual languages while requiring significantly less disk space. Additionally, we show that our adapted PLM also improves the zero-shot cross-lingual transfer abilities of parameter efficient fine-tuning methods.

null

28,826

inproceedings

byamugisha-2022-noun

Noun Class Disambiguation in {R}unyankore and Related Languages

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.383/

Byamugisha, Joan

Proceedings of the 29th International Conference on Computational Linguistics

4350--4359

Bantu languages are spoken by communities in more than half of the countries on the African continent by an estimated third of a billion people. Despite this populous and the amount of high quality linguistic research done over the years, Bantu languages are still computationally under-resourced. The biggest limitation to the development of computational methods for processing Bantu language text is their complex grammatical structure, chiefly in the system of noun classes. We investigated the use of a combined syntactic and semantic method to disambiguate among singular nouns with the same class prefix but belonging to different noun classes. This combination uses the semantic generalizations of the types of nouns in each class to overcome the limitations of relying only on the prefixes they take. We used the nearest neighbors of a query word as semantic generalizations, and developed a tool to determine the noun class based on resources in Runyankore, a Bantu language indigenous to Uganda. We also investigated whether, with the same Runyankore resources, our method had utility in other Bantu languages, Luganda, indigenous to Uganda, and Kinyarwanda, indigenous to Rwanda. For all three languages, the combined approach resulted in an improvement in accuracy, as compared to using only the syntactic or the semantic approach.

null

28,827

inproceedings

evang-etal-2022-improving

Improving Low-resource {RRG} Parsing with Cross-lingual Self-training

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.384/

Evang, Kilian and Kallmeyer, Laura and Waszczuk, Jakub and von Prince, Kilu and Bladier, Tatiana and Petitjean, Simon

Proceedings of the 29th International Conference on Computational Linguistics

4360--4371

This paper considers the task of parsing low-resource languages in a scenario where parallel English data and also a limited seed of annotated sentences in the target language are available, as for example in bootstrapping parallel treebanks. We focus on constituency parsing using Role and Reference Grammar (RRG), a theory that has so far been understudied in computational linguistics but that is widely used in typological research, i.e., in particular in the context of low-resource languages. Starting from an existing RRG parser, we propose two strategies for low-resource parsing: first, we extend the parsing model into a cross-lingual parser, exploiting the parallel data in the high-resource language and unsupervised word alignments by providing internal states of the source-language parser to the target-language parser. Second, we adopt self-training, thereby iteratively expanding the training data, starting from the seed, by including the most confident new parses in each round. Both in simulated scenarios and with a real low-resource language (Daakaka), we find substantial and complementary improvements from both self-training and cross-lingual parsing. Moreover, we also experimented with using gloss embeddings in addition to token embeddings in the target language, and this also improves results. Finally, starting from what we have for Daakaka, we also consider parsing a related language (Dalkalaen) where glosses and English translations are available but no annotated trees at all, i.e., a no-resource scenario wrt. syntactic annotations. We start with cross-lingual parser trained on Daakaka with glosses and use self-training to adapt it to Dalkalaen. The results are surprisingly good.

null

28,828

inproceedings

ding-etal-2022-simple

A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.385/

Ding, Kunbo and Liu, Weijie and Fang, Yuejian and Mao, Weiquan and Zhao, Zhe and Zhu, Tao and Liu, Haoyan and Tian, Rong and Chen, Yiren

Proceedings of the 29th International Conference on Computational Linguistics

4372--4380

Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages. To disengage from these dependencies, researchers have explored training multilingual models on English-only resources and transferring them to low-resource languages. However, its effect is limited by the gap between embedding clusters of different languages. To address this issue, we propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embeddings without semantic loss, thereby improving cross-lingual transferability. Experimental results on mBERT and XLM-R demonstrate that our method significantly outperforms previous works on the zero-shot cross-lingual text classification task and can obtain a better multilingual alignment.

null

28,829

inproceedings

liu-etal-2022-towards-multi

Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.386/

Liu, Linlin and Nguyen, Thien Hai and Joty, Shafiq and Bing, Lidong and Si, Luo

Proceedings of the 29th International Conference on Computational Linguistics

4381--4396

Cross-lingual word embeddings (CLWE) have been proven useful in many cross-lingual tasks. However, most existing approaches to learn CLWE including the ones with contextual embeddings are sense agnostic. In this work, we propose a novel framework to align contextual embeddings at the sense level by leveraging cross-lingual signal from bilingual dictionaries only. We operationalize our framework by first proposing a novel sense-aware cross entropy loss to model word senses explicitly. The monolingual ELMo and BERT models pretrained with our sense-aware cross entropy loss demonstrate significant performance improvement for word sense disambiguation tasks. We then propose a sense alignment objective on top of the sense-aware cross entropy loss for cross-lingual model pretraining, and pretrain cross-lingual models for several language pairs (English to German/Spanish/Japanese/Chinese). Compared with the best baseline results, our cross-lingual models achieve 0.52{\%}, 2.09{\%} and 1.29{\%} average performance improvements on zero-shot cross-lingual NER, sentiment classification and XNLI tasks, respectively.

null

28,830

inproceedings

mompelat-etal-2022-parse

How to Parse a Creole: When Martinican Creole Meets {F}rench

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.387/

Mompelat, Ludovic and Dakota, Daniel and K{\"ubler, Sandra

Proceedings of the 29th International Conference on Computational Linguistics

4397--4406

We investigate methods to develop a parser for Martinican Creole, a highly under-resourced language, using a French treebank. We compare transfer learning and multi-task learning models and examine different input features and strategies to handle the massive size imbalance between the treebanks. Surprisingly, we find that a simple concatenated (French + Martinican Creole) baseline yields optimal results even though it has access to only 80 Martinican Creole sentences. POS embeddings work better than lexical ones, but they suffer from negative transfer.

null

28,831

inproceedings

zhang-xu-2022-byte

Byte-based Multilingual {NMT} for Endangered Languages

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.388/

Zhang, Mengjiao and Xu, Jia

Proceedings of the 29th International Conference on Computational Linguistics

4407--4417

Multilingual neural machine translation (MNMT) jointly trains a shared model for translation with multiple language pairs. However, traditional subword-based MNMT approaches suffer from out-of-vocabulary (OOV) issues and representation bottleneck, which often degrades translation performance on certain language pairs. While byte tokenization is used to tackle the OOV problems in neural machine translation (NMT), until now its capability has not been validated in MNMT. Additionally, existing work has not studied how byte encoding can benefit endangered language translation to our knowledge. We propose a byte-based multilingual neural machine translation system (BMNMT) to alleviate the representation bottleneck and improve translation performance in endangered languages. Furthermore, we design a random byte mapping method with an ensemble prediction to enhance our model robustness. Experimental results show that our BMNMT consistently and significantly outperforms subword/word-based baselines on twelve language pairs up to +18.5 BLEU points, an 840{\%} relative improvement.

null

28,832

inproceedings

romadhona-etal-2022-brcc

{BRCC} and {S}enti{B}ahasa{R}ojak: The First {B}ahasa Rojak Corpus for Pretraining and Sentiment Analysis Dataset

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.389/

Romadhona, Nanda Putri and Lu, Sin-En and Lu, Bo-Han and Tsai, Richard Tzong-Han

Proceedings of the 29th International Conference on Computational Linguistics

4418--4428

Code-mixing refers to the mixed use of multiple languages. It is prevalent in multilingual societies and is also one of the most challenging natural language processing tasks. In this paper, we study Bahasa Rojak, a dialect popular in Malaysia that consists of English, Malay, and Chinese. Aiming to establish a model to deal with the code-mixing phenomena of Bahasa Rojak, we use data augmentation to automatically construct the first Bahasa Rojak corpus for pre-training language models, which we name the Bahasa Rojak Crawled Corpus (BRCC). We also develop a new pre-trained model called {\textquotedblleft}Mixed XLM{\textquotedblright}. The model can tag the language of the input token automatically to process code-mixing input. Finally, to test the effectiveness of the Mixed XLM model pre-trained on BRCC for social media scenarios where code-mixing is found frequently, we compile a new Bahasa Rojak sentiment analysis dataset, SentiBahasaRojak, with a Kappa value of 0.77.

null

28,833

inproceedings

melgarejo-etal-2022-wordnet

{W}ord{N}et-{QU}: Development of a Lexical Database for {Q}uechua Varieties

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.390/

Melgarejo, Nelsi and Zevallos, Rodolfo and Gomez, Hector and Ortega, John E.

Proceedings of the 29th International Conference on Computational Linguistics

4429--4433

In the effort to minimize the risk of extinction of a language, linguistic resources are fundamental. Quechua, a low-resource language from South America, is a language spoken by millions but, despite several efforts in the past, still lacks the resources necessary to build high-performance computational systems. In this article, we present WordNet-QU which signifies the inclusion of Quechua in a well-known lexical database called wordnet. We propose WordNet-QU to be included as an extension to wordnet after demonstrating a manually-curated collection of multiple digital resources for lexical use in Quechua. Our work uses the synset alignment algorithm to compare Quechua to its geographically nearest high-resource language, Spanish. Altogether, we propose a total of 28,582 unique synset IDs divided according to region like so: 20510 for Southern Quechua, 5993 for Central Quechua, 1121 for Northern Quechua, and 958 for Amazonian Quechua.

null

28,834

inproceedings

singh-lefever-2022-student

When the Student Becomes the Master: Learning Better and Smaller Monolingual Models from m{BERT}

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.391/

Singh, Pranaydeep and Lefever, Els

Proceedings of the 29th International Conference on Computational Linguistics

4434--4441

In this research, we present pilot experiments to distil monolingual models from a jointly trained model for 102 languages (mBERT). We demonstrate that it is possible for the target language to outperform the original model, even with a basic distillation setup. We evaluate our methodology for 6 languages with varying amounts of resources and language families.

null

28,835

inproceedings

kundu-etal-2022-zero

Zero-shot Disfluency Detection for {I}ndian Languages

Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon

oct

2022

Gyeongju, Republic of Korea

International Committee on Computational Linguistics

https://aclanthology.org/2022.coling-1.392/

Kundu, Rohit and Jyothi, Preethi and Bhattacharyya, Pushpak

Proceedings of the 29th International Conference on Computational Linguistics

4442--4454

Disfluencies that appear in the transcriptions from automatic speech recognition systems tend to impair the performance of downstream NLP tasks. Disfluency correction models can help alleviate this problem. However, the unavailability of labeled data in low-resource languages impairs progress. We propose using a pretrained multilingual model, finetuned only on English disfluencies, for zero-shot disfluency detection in Indian languages. We present a detailed pipeline to synthetically generate disfluent text and create evaluation datasets for four Indian languages: Bengali, Hindi, Malayalam, and Marathi. Even in the zero-shot setting, we obtain F1 scores of 75 and higher on five disfluency types across all four languages. We also show the utility of synthetically generated disfluencies by evaluating on real disfluent text in Bengali, Hindi, and Marathi. Finetuning the multilingual model on additional synthetic Hindi disfluent text nearly doubles the number of exact matches and yields a 20-point boost in F1 scores when evaluated on real Hindi disfluent text, compared to training with only English disfluent text.

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