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

inproceedings

laban-etal-2022-quiz

Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.9/

Laban, Philippe and Wu, Chien-Sheng and Murakhovs{'}ka, Lidiya and Liu, Wenhao and Xiong, Caiming

Findings of the Association for Computational Linguistics: NAACL 2022

102--111

Question generation (QGen) models are often evaluated with standardized NLG metrics that are based on n-gram overlap. In this paper, we measure whether these metric improvements translate to gains in a practical setting, focusing on the use case of helping teachers automate the generation of reading comprehension quizzes. In our study, teachers building a quiz receive question suggestions, which they can either accept or refuse with a reason. Even though we find that recent progress in QGen leads to a significant increase in question acceptance rates, there is still large room for improvement, with the best model having only 68.4{\%} of its questions accepted by the ten teachers who participated in our study. We then leverage the annotations we collected to analyze standard NLG metrics and find that model performance has reached projected upper-bounds, suggesting new automatic metrics are needed to guide QGen research forward.

null

10.18653/v1/2022.findings-naacl.9

null

26,266

inproceedings

parmar-etal-2022-boxbart

In-{B}o{XBART}: Get Instructions into Biomedical Multi-Task Learning

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.10/

Parmar, Mihir and Mishra, Swaroop and Purohit, Mirali and Luo, Man and Mohammad, Murad and Baral, Chitta

Findings of the Association for Computational Linguistics: NAACL 2022

112--128

Single-task models have proven pivotal in solving specific tasks; however, they have limitations in real-world applications where multi-tasking is necessary and domain shifts are exhibited. Recently, instructional prompts have shown significant improvement towards multi-task generalization; however, the effect of instructional prompts and Multi-Task Learning (MTL) has not been systematically studied in the biomedical domain. Motivated by this, this paper explores the impact of instructional prompts for biomedical MTL. We introduce the BoX, a collection of 32 instruction tasks for Biomedical NLP across (X) various categories. Using this meta-dataset, we propose a unified model termed as In-BoXBART, that can jointly learn all tasks of the BoX without any task-specific modules. To the best of our knowledge, this is the first attempt to propose a unified model in the biomedical domain and use instructions to achieve generalization across several biomedical tasks. Experimental results indicate that the proposed model: 1) outperforms single-task baseline by {\textasciitilde}3{\%} and multi-task (without instruction) baseline by {\textasciitilde}18{\%} on an average, and 2) shows {\textasciitilde}23{\%} improvement compared to single-task baseline in few-shot learning (i.e., 32 instances per task) on an average. Our analysis indicates that there is significant room for improvement across tasks in the BoX, implying the scope for future research direction.

null

10.18653/v1/2022.findings-naacl.10

null

26,267

inproceedings

jundi-lapesa-2022-translate

How to Translate Your Samples and Choose Your Shots? Analyzing Translate-train {\&} Few-shot Cross-lingual Transfer

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.11/

Jundi, Iman and Lapesa, Gabriella

Findings of the Association for Computational Linguistics: NAACL 2022

129--150

Translate-train or few-shot cross-lingual transfer can be used to improve the zero-shot performance of multilingual pretrained language models. Few-shot utilizes high-quality low-quantity samples (often manually translated from the English corpus ). Translate-train employs a machine translation of the English corpus, resulting in samples with lower quality that could be scaled to high quantity. Given the lower cost and higher availability of machine translation compared to manual professional translation, it is important to systematically compare few-shot and translate-train, understand when each has an advantage, and investigate how to choose the shots to translate in order to increase the few-shot gain. This work aims to fill this gap: we compare and quantify the performance gain of few-shot vs. translate-train using three different base models and a varying number of samples for three tasks/datasets (XNLI, PAWS-X, XQuAD) spanning 17 languages. We show that scaling up the training data using machine translation gives a larger gain compared to using the small-scale (higher-quality) few-shot data. When few-shot is beneficial, we show that there are random sets of samples that perform better across languages and that the performance on English and on the machine-translation of the samples can both be used to choose the shots to manually translate for an increased few-shot gain.

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10.18653/v1/2022.findings-naacl.11

null

26,268

inproceedings

feng-etal-2022-multi

Multi-Hop Open-Domain Question Answering over Structured and Unstructured Knowledge

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.12/

Feng, Yue and Han, Zhen and Sun, Mingming and Li, Ping

Findings of the Association for Computational Linguistics: NAACL 2022

151--156

Open-domain question answering systems need to answer question of our interests with structured and unstructured information. However, existing approaches only select one source to generate answer or only conduct reasoning on structured information. In this paper, we pro- pose a Document-Entity Heterogeneous Graph Network, referred to as DEHG, to effectively integrate different sources of information, and conduct reasoning on heterogeneous information. DEHG employs a graph constructor to integrate structured and unstructured information, a context encoder to represent nodes and question, a heterogeneous information reasoning layer to conduct multi-hop reasoning on both information sources, and an answer decoder to generate answers for the question. Experimental results on HybirdQA dataset show that DEHG outperforms the state-of-the-art methods.

null

10.18653/v1/2022.findings-naacl.12

null

26,269

inproceedings

lin-etal-2022-fednlp

{F}ed{NLP}: Benchmarking Federated Learning Methods for Natural Language Processing Tasks

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.13/

Lin, Bill Yuchen and He, Chaoyang and Ze, Zihang and Wang, Hulin and Hua, Yufen and Dupuy, Christophe and Gupta, Rahul and Soltanolkotabi, Mahdi and Ren, Xiang and Avestimehr, Salman

Findings of the Association for Computational Linguistics: NAACL 2022

157--175

Increasing concerns and regulations about data privacy and sparsity necessitate the study of privacy-preserving, decentralized learning methods for natural language processing (NLP) tasks. Federated learning (FL) provides promising approaches for a large number of clients (e.g., personal devices or organizations) to collaboratively learn a shared global model to benefit all clients while allowing users to keep their data locally. Despite interest in studying FL methods for NLP tasks, a systematic comparison and analysis is lacking in the literature. Herein, we present the FedNLP, a benchmarking framework for evaluating federated learning methods on four different task formulations: text classification, sequence tagging, question answering, and seq2seq. We propose a universal interface between Transformer-based language models (e.g., BERT, BART) and FL methods (e.g., FedAvg, FedOPT, etc.) under various non-IID partitioning strategies. Our extensive experiments with FedNLP provide empirical comparisons between FL methods and help us better understand the inherent challenges of this direction. The comprehensive analysis points to intriguing and exciting future research aimed at developing FL methods for NLP tasks.

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10.18653/v1/2022.findings-naacl.13

null

26,270

inproceedings

wang-etal-2022-semattack

{S}em{A}ttack: Natural Textual Attacks via Different Semantic Spaces

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.14/

Wang, Boxin and Xu, Chejian and Liu, Xiangyu and Cheng, Yu and Li, Bo

Findings of the Association for Computational Linguistics: NAACL 2022

176--205

Recent studies show that pre-trained language models (LMs) are vulnerable to textual adversarial attacks. However, existing attack methods either suffer from low attack success rates or fail to search efficiently in the exponentially large perturbation space. We propose an efficient and effective framework SemAttack to generate natural adversarial text by constructing different semantic perturbation functions. In particular, SemAttack optimizes the generated perturbations constrained on generic semantic spaces, including typo space, knowledge space (e.g., WordNet), contextualized semantic space (e.g., the embedding space of BERT clusterings), or the combination of these spaces. Thus, the generated adversarial texts are more semantically close to the original inputs. Extensive experiments reveal that state-of-the-art (SOTA) large-scale LMs (e.g., DeBERTa-v2) and defense strategies (e.g., FreeLB) are still vulnerable to SemAttack. We further demonstrate that SemAttack is general and able to generate natural adversarial texts for different languages (e.g., English and Chinese) with high attack success rates. Human evaluations also confirm that our generated adversarial texts are natural and barely affect human performance. Our code is publicly available at \url{https://github.com/AI-secure/SemAttack}.

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10.18653/v1/2022.findings-naacl.14

null

26,271

inproceedings

vogler-etal-2022-lacuna

Lacuna Reconstruction: Self-Supervised Pre-Training for Low-Resource Historical Document Transcription

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.15/

Vogler, Nikolai and Allen, Jonathan and Miller, Matthew and Berg-Kirkpatrick, Taylor

Findings of the Association for Computational Linguistics: NAACL 2022

206--216

We present a self-supervised pre-training approach for learning rich visual language representations for both handwritten and printed historical document transcription. After supervised fine-tuning of our pre-trained encoder representations for low-resource document transcription on two languages, (1) a heterogeneous set of handwritten Islamicate manuscript images and (2) early modern English printed documents, we show a meaningful improvement in recognition accuracy over the same supervised model trained from scratch with as few as 30 line image transcriptions for training. Our masked language model-style pre-training strategy, where the model is trained to be able to identify the true masked visual representation from distractors sampled from within the same line, encourages learning robust contextualized language representations invariant to scribal writing style and printing noise present across documents.

null

10.18653/v1/2022.findings-naacl.15

null

26,272

inproceedings

guo-etal-2022-freetransfer

{F}ree{T}ransfer-{X}: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.16/

Guo, Yinpeng and Li, Liangyou and Jiang, Xin and Liu, Qun

Findings of the Association for Computational Linguistics: NAACL 2022

217--228

Cross-lingual transfer (CLT) is of various applications. However, labeled cross-lingual corpus is expensive or even inaccessible, especially in the fields where labels are private, such as diagnostic results of symptoms in medicine and user profiles in business. Nevertheless, there are off-the-shelf models in these sensitive fields. Instead of pursuing the original labels, a workaround for CLT is to transfer knowledge from the off-the-shelf models without labels. To this end, we define a novel CLT problem named FreeTransfer-X that aims to achieve knowledge transfer from the off-the-shelf models in rich-resource languages. To address the problem, we propose a 2-step knowledge distillation (KD, Hinton et al., 2015) framework based on multilingual pre-trained language models (mPLM). The significant improvement over strong neural machine translation (NMT) baselines demonstrates the effectiveness of the proposed method. In addition to reducing annotation cost and protecting private labels, the proposed method is compatible with different networks and easy to be deployed. Finally, a range of analyses indicate the great potential of the proposed method.

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10.18653/v1/2022.findings-naacl.16

null

26,273

inproceedings

liebling-etal-2022-opportunities

Opportunities for Human-centered Evaluation of Machine Translation Systems

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.17/

Liebling, Daniel and Heller, Katherine and Robertson, Samantha and Deng, Wesley

Findings of the Association for Computational Linguistics: NAACL 2022

229--240

Machine translation models are embedded in larger user-facing systems. Although model evaluation has matured, evaluation at the systems level is still lacking. We review literature from both the translation studies and HCI communities about who uses machine translation and for what purposes. We emphasize an important difference in evaluating machine translation models versus the physical and cultural systems in which they are embedded. We then propose opportunities for improved measurement of user-facing translation systems. We pay particular attention to the need for design and evaluation to aid engendering trust and enhancing user agency in future machine translation systems.

null

10.18653/v1/2022.findings-naacl.17

null

26,274

inproceedings

liu-etal-2022-aligning

Aligning Generative Language Models with Human Values

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.18/

Liu, Ruibo and Zhang, Ge and Feng, Xinyu and Vosoughi, Soroush

Findings of the Association for Computational Linguistics: NAACL 2022

241--252

Although current large-scale generative language models (LMs) can show impressive insights about factual knowledge, they do not exhibit similar success with respect to human values judgements (e.g., whether or not the generations of an LM are moral). Existing methods learn human values either by directly mimicking the behavior of human data, or rigidly constraining the generation space to human-chosen tokens. These methods are inherently limited in that they do not consider the contextual and abstract nature of human values and as a result often fail when dealing with out-of-domain context or sophisticated and abstract human values. This paper proposes SENSEI, a new reinforcement learning based method that can embed human values judgements into each step of language generation. SENSEI deploys an Actor-Critic framework, where the Critic is a reward distributor that simulates the reward assignment procedure of humans, while the Actor guides the generation towards the maximum reward direction. Compared with five existing methods in three human values alignment datasets, SENSEI not only achieves higher alignment performance in terms of both automatic and human evaluations, but also shows improvements on robustness and transfer learning on unseen human values.

null

10.18653/v1/2022.findings-naacl.18

null

26,275

inproceedings

dutt-etal-2022-perkgqa

{P}er{KGQA}: Question Answering over Personalized Knowledge Graphs

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.19/

Dutt, Ritam and Bhattacharjee, Kasturi and Gangadharaiah, Rashmi and Roth, Dan and Rose, Carolyn

Findings of the Association for Computational Linguistics: NAACL 2022

253--268

Previous studies on question answering over knowledge graphs have typically operated over a single knowledge graph (KG). This KG is assumed to be known a priori and is lever- aged similarly for all users' queries during inference. However, such an assumption is not applicable to real-world settings, such as health- care, where one needs to handle queries of new users over unseen KGs during inference. Furthermore, privacy concerns and high computational costs render it infeasible to query the single KG that has information about all users while answering a specific user`s query. The above concerns motivate our question answer- ing setting over personalized knowledge graphs (PERKGQA) where each user has restricted access to their KG. We observe that current state-of-the-art KGQA methods that require learning prior node representations fare poorly. We propose two complementary approaches, PATHCBR and PATHRGCN for PERKGQA. The former is a simple non-parametric technique that employs case-based reasoning, while the latter is a parametric approach using graph neural networks. Our proposed methods circumvent learning prior representations, can generalize to unseen KGs, and outperform strong baselines on an academic and an internal dataset by 6.5{\%} and 10.5{\%}.

null

10.18653/v1/2022.findings-naacl.19

null

26,276

inproceedings

wu-etal-2022-zero-shot

Zero-shot Cross-lingual Conversational Semantic Role Labeling

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.20/

Wu, Han and Tan, Haochen and Xu, Kun and Liu, Shuqi and Wu, Lianwei and Song, Linqi

Findings of the Association for Computational Linguistics: NAACL 2022

269--281

While conversational semantic role labeling (CSRL) has shown its usefulness on Chinese conversational tasks, it is still under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training. To avoid expensive data collection and error-propagation of translation-based methods, we present a simple but effective approach to perform zero-shot cross-lingual CSRL.Our model implicitly learns language-agnostic, conversational structure-aware and semantically rich representations with the hierarchical encoders and elaborately designed pre-training objectives. Experimental results show that our model outperforms all baselines by large margins on two newly collected English CSRL test sets. More importantly, we confirm the usefulness of CSRL to non-Chinese conversational tasks such as the question-in-context rewriting task in English and the multi-turn dialogue response generation tasks in English, German and Japanese by incorporating the CSRL information into the downstream conversation-based models. We believe this finding is significant and will facilitate the research of non-Chinese dialogue tasks which suffer the problems of ellipsis and anaphora.

null

10.18653/v1/2022.findings-naacl.20

null

26,277

inproceedings

mullick-etal-2022-framework

A Framework to Generate High-Quality Datapoints for Multiple Novel Intent Detection

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.21/

Mullick, Ankan and Purkayastha, Sukannya and Goyal, Pawan and Ganguly, Niloy

Findings of the Association for Computational Linguistics: NAACL 2022

282--292

Systems like Voice-command based conversational agents are characterized by a pre-defined set of skills or intents to perform user specified tasks. In the course of time, newer intents may emerge requiring retraining. However, the newer intents may not be explicitly announced and need to be inferred dynamically. Thus, there are two important tasks at hand (a). identifying emerging new intents, (b). annotating data of the new intents so that the underlying classifier can be retrained efficiently. The tasks become specially challenging when a large number of new intents emerge simultaneously and there is a limited budget of manual annotation. In this paper, we propose MNID (Multiple Novel Intent Detection) which is a cluster based framework to detect multiple novel intents with budgeted human annotation cost. Empirical results on various benchmark datasets (of different sizes) demonstrate that MNID, by intelligently using the budget for annotation, outperforms the baseline methods in terms of accuracy and F1-score.

null

10.18653/v1/2022.findings-naacl.21

null

26,278

inproceedings

chen-etal-2022-design

Design Challenges for a Multi-Perspective Search Engine

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.22/

Chen, Sihao and Liu, Siyi and Uyttendaele, Xander and Zhang, Yi and Bruno, William and Roth, Dan

Findings of the Association for Computational Linguistics: NAACL 2022

293--303

Many users turn to document retrieval systems (e.g. search engines) to seek answers to controversial or open-ended questions. However, classical document retrieval systems fall short at delivering users a set of direct and diverse responses in such cases, which requires identifying responses within web documents in the context of the query, and aggregating the responses based on their different perspectives. The goal of this work is to survey and study the user information needs for building a multi-perspective search engine of such. We examine the challenges of synthesizing such language understanding objectives with document retrieval, and study a new \textit{perspective-oriented} document retrieval paradigm. We discuss and assess the inherent natural language understanding challenges one needs to address in order to achieve the goal. Following the design challenges and principles, we propose and evaluate a practical prototype pipeline system. We use the prototype system to conduct a user survey in order to assess the utility of our paradigm, as well as understanding the user information needs when issuing controversial and open-ended queries to a search engine.

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10.18653/v1/2022.findings-naacl.22

null

26,279

inproceedings

ortiz-etal-2022-exploring

Exploring the Value of Multi-View Learning for Session-Aware Query Representation

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.23/

Ortiz, Diego and Moreno, Jose and Hubert, Gilles and Pinel-Sauvagnat, Karen and Tamine, Lynda

Findings of the Association for Computational Linguistics: NAACL 2022

304--315

Recent years have witnessed a growing interest towards learning distributed query representations that are able to capture search intent semantics. Most existing approaches learn query embeddings using relevance supervision making them suited only to document ranking tasks. Besides, they generally consider either user`s query reformulations or system`s rankings whereas previous findings show that user`s query behavior and knowledge change depending on the system`s results, intertwine and affect each other during the completion of a search task. In this paper, we explore the value of multi-view learning for generic and unsupervised session-aware query representation learning. First, single-view query embeddings are obtained in separate spaces from query reformulations and document ranking representations using transformers. Then, we investigate the use of linear (CCA) and non linear (UMAP) multi-view learning methods, to align those spaces with the aim of revealing similarity traits in the multi-view shared space. Experimental evaluation is carried out in a query classification and session-based retrieval downstream tasks using respectively the KDD and TREC session datasets. The results show that multi-view learning is an effective and controllable approach for unsupervised learning of generic query representations and can reflect search behavior patterns.

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10.18653/v1/2022.findings-naacl.23

null

26,280

inproceedings

li-etal-2022-hierarchical

Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.24/

Li, Yang and Long, Guodong and Shen, Tao and Jiang, Jing

Findings of the Association for Computational Linguistics: NAACL 2022

316--326

Distant supervision uses triple facts in knowledge graphs to label a corpus for relation extraction, leading to wrong labeling and long-tail problems. Some works use the hierarchy of relations for knowledge transfer to long-tail relations. However, a coarse-grained relation often implies only an attribute (e.g., domain or topic) of the distant fact, making it hard to discriminate relations based solely on sentence semantics. One solution is resorting to entity types, but open questions remain about how to fully leverage the information of entity types and how to align multi-granular entity types with sentences. In this work, we propose a novel model to enrich distantly-supervised sentences with entity types. It consists of (1) a pairwise type-enriched sentence encoding module injecting both context-free and -related backgrounds to alleviate sentence-level wrong labeling, and (2) a hierarchical type-sentence alignment module enriching a sentence with the triple fact`s basic attributes to support long-tail relations. Our model achieves new state-of-the-art results in overall and long-tail performance on benchmarks.

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10.18653/v1/2022.findings-naacl.24

null

26,281

inproceedings

zhang-etal-2022-pcee

{PCEE}-{BERT}: Accelerating {BERT} Inference via Patient and Confident Early Exiting

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.25/

Zhang, Zhen and Zhu, Wei and Zhang, Jinfan and Wang, Peng and Jin, Rize and Chung, Tae-Sun

Findings of the Association for Computational Linguistics: NAACL 2022

327--338

BERT and other pretrained language models (PLMs) are ubiquitous in modern NLP. Even though PLMs are the state-of-the-art (SOTA) models for almost every NLP task (CITATION), the significant latency during inference prohibits wider industrial usage. In this work, we propose Patient and Confident Early Exiting BERT (PCEE-BERT), an off-the-shelf sample-dependent early exiting method that can work with different PLMs and can also work along with popular model compression methods. With a multi-exit BERT as the backbone model, PCEE-BERT will make the early exiting decision if enough numbers (patience parameter) of consecutive intermediate layers are confident about their predictions. The entropy value measures the confidence level of an intermediate layer`s prediction. Experiments on the GLUE benchmark demonstrate that our method outperforms previous SOTA early exiting methods. Ablation studies show that: (a) our method performs consistently well on other PLMs, such as ALBERT and TinyBERT; (b) PCEE-BERT can achieve different speed-up ratios by adjusting the patience parameter and the confidence threshold. The code for PCEE-BERT can be found at \url{https://github.com/michael-wzhu/PCEE-BERT}.

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10.18653/v1/2022.findings-naacl.25

null

26,282

inproceedings

tandon-etal-2022-learning

Learning to repair: Repairing model output errors after deployment using a dynamic memory of feedback

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.26/

Tandon, Niket and Madaan, Aman and Clark, Peter and Yang, Yiming

Findings of the Association for Computational Linguistics: NAACL 2022

339--352

Large language models (LMs), while powerful, are not immune to mistakes, but can be difficult to retrain. Our goal is for an LM to continue to improve after deployment, without retraining, using feedback from the user. Our approach pairs an LM with (i) a growing memory of cases where the user identified an output error and provided general feedback on how to correct it (ii) a \textit{corrector model}, trained to translate this general feedback into specific edits to repair the model output. Given a new, unseen input, our model can then use feedback from similar, past cases to repair output errors that may occur. We instantiate our approach using an existing, fixed model for \textit{script generation}, that takes a goal (e.g., {\textquotedblleft}bake a cake{\textquotedblright}) and generates a partially ordered sequence of actions to achieve that goal, sometimes containing errors. Our memory-enhanced system, , learns to apply user feedback to repair such errors (up to 30 points improvement), while making a start at avoiding similar past mistakes on new, unseen examples (up to 7 points improvement in a controlled setting). This is a first step towards strengthening deployed models, potentially broadening their utility. Our code and data is available at \url{https://github.com/allenai/interscript}

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10.18653/v1/2022.findings-naacl.26

null

26,283

inproceedings

gooding-tragut-2022-one

One Size Does Not Fit All: The Case for Personalised Word Complexity Models

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.27/

Gooding, Sian and Tragut, Manuel

Findings of the Association for Computational Linguistics: NAACL 2022

353--365

Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand. It has been shown that CWI systems can improve text simplification, readability prediction and vocabulary acquisition modelling. However, the difficulty of a word is a highly idiosyncratic notion that depends on a reader`s first language, proficiency and reading experience. In this paper, we show that personal models are best when predicting word complexity for individual readers. We use a novel active learning framework that allows models to be tailored to individuals and release a dataset of complexity annotations and models as a benchmark for further research.

null

10.18653/v1/2022.findings-naacl.27

null

26,284

inproceedings

phukon-etal-2022-team

{TEAM}: A multitask learning based Taxonomy Expansion approach for Attach and Merge

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.28/

Phukon, Bornali and Mitra, Anasua and Sanasam, Ranbir and Sarmah, Priyankoo

Findings of the Association for Computational Linguistics: NAACL 2022

366--378

Taxonomy expansion is a crucial task. Most of Automatic expansion of taxonomy are of two types, attach and merge. In a taxonomy like WordNet, both merge and attach are integral parts of the expansion operations but majority of study consider them separately. This paper proposes a novel mult-task learning-based deep learning method known as Taxonomy Expansion with Attach and Merge (TEAM) that performs both the merge and attach operations. To the best of our knowledge this is the first study which integrates both merge and attach operations in a single model. The proposed models have been evaluated on three separate WordNet taxonomies, viz., Assamese, Bangla, and Hindi. From the various experimental setups, it is shown that TEAM outperforms its state-of-the-art counterparts for attach operation, and also provides highly encouraging performance for the merge operation.

null

10.18653/v1/2022.findings-naacl.28

null

26,285

inproceedings

zhang-etal-2022-extracting

Extracting Temporal Event Relation with Syntax-guided Graph Transformer

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.29/

Zhang, Shuaicheng and Ning, Qiang and Huang, Lifu

Findings of the Association for Computational Linguistics: NAACL 2022

379--390

Extracting temporal relations (e.g., before, after, and simultaneous) among events is crucial to natural language understanding. One of the key challenges of this problem is that when the events of interest are far away in text, the context in-between often becomes complicated, making it challenging to resolve the temporal relationship between them. This paper thus proposes a new Syntax-guided Graph Transformer network (SGT) to mitigate this issue, by (1) explicitly exploiting the connection between two events based on their dependency parsing trees, and (2) automatically locating temporal cues between two events via a novel syntax-guided attention mechanism. Experiments on two benchmark datasets, MATRES and TB-DENSE, show that our approach significantly outperforms previous state-of-the-art methods on both end-to-end temporal relation extraction and temporal relation classification with up to 7.9{\%} absolute F-score gain; This improvement also proves to be robust on the contrast set of MATRES. We will make all the programs publicly available once the paper is accepted.

null

10.18653/v1/2022.findings-naacl.29

null

26,286

inproceedings

mar-liu-2022-cognitive

From Cognitive to Computational Modeling: {T}ext-based Risky Decision-Making Guided by Fuzzy Trace Theory

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.30/

Mar, Jaron and Liu, Jiamou

Findings of the Association for Computational Linguistics: NAACL 2022

391--409

Understanding, modelling and predicting human risky decision-making is challenging due to intrinsic individual differences and irrationality. Fuzzy trace theory (FTT) is a powerful paradigm that explains human decision-making by incorporating gists, i.e., fuzzy representations of information which capture only its quintessential meaning. Inspired by Broniatowski and Reyna`s FTT cognitive model, we propose a computational framework which combines the effects of the underlying semantics and sentiments on text-based decision-making. In particular, we introduce Category-2-Vector to learn categorical gists and categorical sentiments, and demonstrate how our computational model can be optimised to predict risky decision-making in groups and individuals.

null

10.18653/v1/2022.findings-naacl.30

null

26,287

inproceedings

marasovic-etal-2022-shot

Few-Shot Self-Rationalization with Natural Language Prompts

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.31/

Marasovic, Ana and Beltagy, Iz and Downey, Doug and Peters, Matthew

Findings of the Association for Computational Linguistics: NAACL 2022

410--424

Self-rationalization models that predict task labels and generate free-text elaborations for their predictions could enable more intuitive interaction with NLP systems. These models are, however, currently trained with a large amount of human-written free-text explanations for each task which hinders their broader usage. We propose to study a more realistic setting of self-rationalization using few training examples. We present FEB{---}a standardized collection of four existing English-language datasets and associated metrics. We identify the right prompting approach by extensively exploring natural language prompts on FEB. Then, by using this prompt and scaling the model size, we demonstrate that making progress on few-shot self-rationalization is possible. We show there is still ample room for improvement in this task: the average plausibility of generated explanations assessed by human annotators is at most 51{\%} (with GPT-3), while plausibility of human explanations is 76{\%}. We hope that FEB and our proposed approach will spur the community to take on the few-shot self-rationalization challenge.

null

10.18653/v1/2022.findings-naacl.31

null

26,288

inproceedings

lee-etal-2022-docmt5

{DOC}m{T}5: Document-Level Pretraining of Multilingual Language Models

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.32/

Lee, Chia-Hsuan and Siddhant, Aditya and Ratnakar, Viresh and Johnson, Melvin

Findings of the Association for Computational Linguistics: NAACL 2022

425--437

In this paper, we introduce DOCmT5, a multilingual sequence-to-sequence language model pretrained with large-scale parallel documents. While previous approaches have focused on leveraging sentence-level parallel data, we try to build a general-purpose pretrained model that can understand and generate long documents. We propose a simple and effective pretraining objective - Document reordering Machine Translation (DrMT), in which the input documents that are shuffled and masked need to be translated. DrMT brings consistent improvements over strong baselines on a variety of document-level generation tasks, including over 12 BLEU points for seen-language pair document-level MT, over 7 BLEU points for unseen-language-pair document-level MT and over 3 ROUGE-1 points for seen-language pair cross-lingual summarization. We achieve state-of-the-art (SOTA) on WMT20 De-En and IWSLT15 Zh-En document translation tasks. We also conduct extensive analysis on various factors for document pretraining, including (1) the effects of pretraining data quality and (2) The effects of combining mono-lingual and cross-lingual pretraining. We plan to make our model checkpoints publicly available.

null

10.18653/v1/2022.findings-naacl.32

null

26,289

inproceedings

naik-etal-2022-literature

Literature-Augmented Clinical Outcome Prediction

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.33/

Naik, Aakanksha and Parasa, Sravanthi and Feldman, Sergey and Wang, Lucy Lu and Hope, Tom

Findings of the Association for Computational Linguistics: NAACL 2022

438--453

We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models. Based on each individual patient`s clinical notes, we train language models (LMs) to find relevant papers and fuse them with information from notes to predict outcomes such as in-hospital mortality. We develop methods to retrieve literature based on noisy, information-dense patient notes, and to augment existing outcome prediction models with retrieved papers in a manner that maximizes predictive accuracy. Our approach boosts predictive performance on three important clinical tasks in comparison to strong recent LM baselines, increasing F1 by up to 5 points and precision@Top-K by a large margin of over 25{\%}.

null

10.18653/v1/2022.findings-naacl.33

null

26,290

inproceedings

zhenzhen-etal-2022-improving

Improving Few-Shot Relation Classification by Prototypical Representation Learning with Definition Text

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.34/

Zhenzhen, Li and Zhang, Yuyang and Nie, Jian-Yun and Li, Dongsheng

Findings of the Association for Computational Linguistics: NAACL 2022

454--464

Few-shot relation classification is difficult because the few instances available may not represent well the relation patterns. Some existing approaches explored extra information such as relation definition, in addition to the instances, to learn a better relation representation. However, the encoding of the extra information has been performed independently from the labeled instances. In this paper, we propose to learn a prototype encoder from relation definition in a way that is useful for relation instance classification. To this end, we use a joint training approach to train both a prototype encoder from definition and an instance encoder. Extensive experiments on several datasets demonstrate the effectiveness and usefulness of our prototype encoder from definition text, enabling us to outperform state-of-the-art approaches.

null

10.18653/v1/2022.findings-naacl.34

null

26,291

inproceedings

neves-ribeiro-etal-2022-entailment

Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.35/

Neves Ribeiro, Danilo and Wang, Shen and Ma, Xiaofei and Dong, Rui and Wei, Xiaokai and Zhu, Henghui and Chen, Xinchi and Xu, Peng and Huang, Zhiheng and Arnold, Andrew and Roth, Dan

Findings of the Association for Computational Linguistics: NAACL 2022

465--475

Large language models have achieved high performance on various question answering (QA) benchmarks, but the explainability of their output remains elusive. Structured explanations, called entailment trees, were recently suggested as a way to explain the reasoning behind a QA system`s answer. In order to better generate such entailment trees, we propose an architecture called Iterative Retrieval-Generation Reasoner (IRGR). Our model is able to explain a given hypothesis by systematically generating a step-by-step explanation from textual premises. The IRGR model iteratively searches for suitable premises, constructing a single entailment step at a time. Contrary to previous approaches, our method combines generation steps and retrieval of premises, allowing the model to leverage intermediate conclusions, and mitigating the input size limit of baseline encoder-decoder models. We conduct experiments using the EntailmentBank dataset, where we outperform existing benchmarks on premise retrieval and entailment tree generation, with around 300{\%} gain in overall correctness.

null

10.18653/v1/2022.findings-naacl.35

null

26,292

inproceedings

maharana-etal-2022-multimodal

Multimodal Intent Discovery from Livestream Videos

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.36/

Maharana, Adyasha and Tran, Quan and Dernoncourt, Franck and Yoon, Seunghyun and Bui, Trung and Chang, Walter and Bansal, Mohit

Findings of the Association for Computational Linguistics: NAACL 2022

476--489

Individuals, educational institutions, and businesses are prolific at generating instructional video content such as {\textquotedblleft}how-to{\textquotedblright} and tutorial guides. While significant progress has been made in basic video understanding tasks, identifying procedural intent within these instructional videos is a challenging and important task that remains unexplored but essential to video summarization, search, and recommendations. This paper introduces the problem of instructional intent identification and extraction from software instructional livestreams. We construct and present a new multimodal dataset consisting of software instructional livestreams and containing manual annotations for both detailed and abstract procedural intent that enable training and evaluation of joint video and text understanding models. We then introduce a multimodal cascaded cross-attention model to efficiently combine the weaker and noisier video signal with the more discriminative text signal. Our experiments show that our proposed model brings significant gains compared to strong baselines, including large-scale pretrained multimodal models. Our analysis further identifies that the task benefits from spatial as well as motion features extracted from videos, and provides insight on how the video signal is preferentially used for intent discovery. We also show that current models struggle to comprehend the nature of abstract intents, revealing important gaps in multimodal understanding and paving the way for future work.

null

10.18653/v1/2022.findings-naacl.36

null

26,293

inproceedings

hossain-etal-2022-question

A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.37/

Hossain, Md Mosharaf and Holman, Luke and Kakileti, Anusha and Kao, Tiffany and Brito, Nathan and Mathews, Aaron and Blanco, Eduardo

Findings of the Association for Computational Linguistics: NAACL 2022

490--503

This paper explores a question-answer driven approach to reveal affirmative interpretations from verbal negations (i.e., when a negation cue grammatically modifies a verb). We create a new corpus consisting of 4,472 verbal negations and discover that 67.1{\%} of them convey that an event actually occurred. Annotators generate and answer 7,277 questions {\%} converted for 4,000 for the 3,001 negations that convey an affirmative interpretation. We first cast the problem of revealing affirmative interpretations from negations as a natural language inference (NLI) classification task. Experimental results show that state-of-the-art transformers trained with existing NLI corpora are insufficient to reveal affirmative interpretations. We also observe, however, that fine-tuning brings substantial improvements. In addition to NLI classification, we also explore the more realistic task of generating affirmative interpretations directly from negations with the T5 transformer. We conclude that the generation task remains a challenge as T5 substantially underperforms humans.

null

10.18653/v1/2022.findings-naacl.37

null

26,294

inproceedings

gong-etal-2022-harmless

Harmless Transfer Learning for Item Embeddings

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.38/

Gong, Chengyue and Du, Xiaocong and Choudhary, Dhruv and Bhushanam, Bhargav and Liu, Qiang and Kejariwal, Arun

Findings of the Association for Computational Linguistics: NAACL 2022

504--516

Learning embedding layers (for classes, words, items, etc.) is a key component of lots of applications, ranging from natural language processing, recommendation systems to electronic health records, etc. However, the frequency of real-world items follows a long-tail distribution in these applications, causing naive training methods perform poorly on the rare items. A line of previous works address this problem by transferring the knowledge from the frequent items to rare items by introducing an auxiliary transfer loss. However, when defined improperly, the transfer loss may introduce harmful biases and deteriorate the performance. In this work, we propose a harmless transfer learning framework that limits the impact of the potential biases in both the definition and optimization of the transfer loss. On the definition side, we reduce the bias in transfer loss by focusing on the items to which information from high-frequency items can be efficiently transferred. On the optimization side, we leverage a lexicographic optimization framework to efficiently incorporate the information of the transfer loss without hurting the minimization of the main prediction loss function. Our method serves as a plug-in module and significantly boosts the performance on a variety of NLP and recommendation system tasks.

null

10.18653/v1/2022.findings-naacl.38

null

26,295

inproceedings

cho-etal-2022-fine

Fine-grained Image Captioning with {CLIP} Reward

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.39/

Cho, Jaemin and Yoon, Seunghyun and Kale, Ajinkya and Dernoncourt, Franck and Bui, Trung and Bansal, Mohit

Findings of the Association for Computational Linguistics: NAACL 2022

517--527

Modern image captioning models are usually trained with text similarity objectives. However, since reference captions in public datasets often describe the most salient common objects, models trained with the text similarity objectives tend to ignore specific and detailed aspects of an image that distinguish it from others. Towards more descriptive and distinctive caption generation, we propose to use CLIP, a multimodal encoder trained on huge image-text pairs from the web, to calculate multi-modal similarity and use it as a reward function. We also propose a simple finetuning strategy of CLIP text encoder to improve grammar that does not require extra text annotation. This completely eliminates the need for reference captions during the reward computation. To comprehensively evaluate descriptive captions, we introduce FineCapEval, a new dataset for caption evaluation with fine-grained criteria: overall, background, object, relations. In our experiments on text-to-image retrieval and FineCapEval, the proposed CLIP-guided model generates more distinctive captions than the CIDEroptimized model. We also show that our unsupervised grammar finetuning of the CLIP text encoder alleviates the degeneration problem of the naive CLIP reward. Lastly, we show human analysis where the annotators strongly prefer CLIP reward to CIDEr and MLE objectives on diverse criteria.

null

10.18653/v1/2022.findings-naacl.39

null

26,296

inproceedings

zhang-etal-2022-improving-faithfulness

Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.40/

Zhang, Haopeng and Yavuz, Semih and Kryscinski, Wojciech and Hashimoto, Kazuma and Zhou, Yingbo

Findings of the Association for Computational Linguistics: NAACL 2022

528--535

Abstractive summarization systems leveraging pre-training language models have achieved superior results on benchmark datasets. However, such models have been shown to be more prone to hallucinate facts that are unfaithful to the input context. In this paper, we propose a method to remedy entity-level extrinsic hallucinations with Entity Coverage Control (ECC). We first compute entity coverage precision and prepend the corresponding control code for each training example, which implicitly guides the model to recognize faithfulness contents in the training phase. We further extend our method via intermediate fine-tuning on large but noisy data extracted from Wikipedia to unlock zero-shot summarization. We show that the proposed method leads to more faithful and salient abstractive summarization in supervised fine-tuning and zero-shot settings according to our experimental results on three benchmark datasets XSum, Pubmed, and SAMSum of very different domains and styles.

null

10.18653/v1/2022.findings-naacl.40

null

26,297

inproceedings

hofmann-etal-2022-modeling

Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.41/

Hofmann, Valentin and Dong, Xiaowen and Pierrehumbert, Janet and Schuetze, Hinrich

Findings of the Association for Computational Linguistics: NAACL 2022

536--550

The increasing polarization of online political discourse calls for computational tools that automatically detect and monitor ideological divides in social media. We introduce a minimally supervised method that leverages the network structure of online discussion forums, specifically Reddit, to detect polarized concepts. We model polarization along the dimensions of salience and framing, drawing upon insights from moral psychology. Our architecture combines graph neural networks with structured sparsity learning and results in representations for concepts and subreddits that capture temporal ideological dynamics such as right-wing and left-wing radicalization.

null

10.18653/v1/2022.findings-naacl.41

null

26,298

inproceedings

akyurek-etal-2022-measuring

On Measuring Social Biases in Prompt-Based Multi-Task Learning

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.42/

Aky{\"urek, Afra Feyza and Paik, Sejin and Kocyigit, Muhammed and Akbiyik, Seda and Runyun, Serife Leman and Wijaya, Derry

Findings of the Association for Computational Linguistics: NAACL 2022

551--564

Large language models trained on a mixture of NLP tasks that are converted into a text-to-text format using prompts, can generalize into novel forms of language and handle novel tasks. A large body of work within prompt engineering attempts to understand the effects of input forms and prompts in achieving superior performance. We consider an alternative measure and inquire whether the way in which an input is encoded affects social biases promoted in outputs. In this paper, we study T0, a large-scale multi-task text-to-text language model trained using prompt-based learning. We consider two different forms of semantically equivalent inputs: question-answer format and premise-hypothesis format. We use an existing bias benchmark for the former BBQ and create the first bias benchmark in natural language inference BBNLI with hand-written hypotheses while also converting each benchmark into the other form. The results on two benchmarks suggest that given two different formulations of essentially the same input, T0 conspicuously acts more biased in question answering form, which is seen during training, compared to premise-hypothesis form which is unlike its training examples. Code and data are released under \url{https://github.com/feyzaakyurek/bbnli}.

null

10.18653/v1/2022.findings-naacl.42

null

26,299

inproceedings

tian-etal-2022-anti

Anti-Overestimation Dialogue Policy Learning for Task-Completion Dialogue System

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.43/

Tian, Chang and Yin, Wenpeng and Moens, Marie-Francine

Findings of the Association for Computational Linguistics: NAACL 2022

565--577

A dialogue policy module is an essential part of task-completion dialogue systems. Recently, increasing interest has focused on reinforcement learning (RL)-based dialogue policy. Its favorable performance and wise action decisions rely on an accurate estimation of action values. The overestimation problem is a widely known issue of RL since its estimate of the maximum action value is larger than the ground truth, which results in an unstable learning process and suboptimal policy. This problem is detrimental to RL-based dialogue policy learning. To mitigate this problem, this paper proposes a dynamic partial average estimator (DPAV) of the ground truth maximum action value. DPAV calculates the partial average between the predicted maximum action value and minimum action value, where the weights are dynamically adaptive and problem-dependent. We incorporate DPAV into a deep Q-network as the dialogue policy and show that our method can achieve better or comparable results compared to top baselines on three dialogue datasets of different domains with a lower computational load. In addition, we also theoretically prove the convergence and derive the upper and lower bounds of the bias compared with those of other methods.

null

10.18653/v1/2022.findings-naacl.43

null

26,300

inproceedings

kulick-etal-2022-penn

{P}enn-{H}elsinki Parsed Corpus of Early {M}odern {E}nglish: First Parsing Results and Analysis

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.44/

Kulick, Seth and Ryant, Neville and Santorini, Beatrice

Findings of the Association for Computational Linguistics: NAACL 2022

578--593

The Penn-Helsinki Parsed Corpus of Early Modern English (PPCEME), a 1.7-million-word treebank that is an important resource for research in syntactic change, has several properties that present potential challenges for NLP technologies. We describe these key features of PPCEME that make it challenging for parsing, including a larger and more varied set of function tags than in the Penn Treebank, and present results for this corpus using a modified version of the Berkeley Neural Parser and the approach to function tag recovery of Gabbard et al. (2006). While this approach to function tag recovery gives reasonable results, it is in some ways inappropriate for span-based parsers. We also present further evidence of the importance of in-domain pretraining for contextualized word representations. The resulting parser will be used to parse Early English Books Online, a 1.5 billion word corpus whose utility for the study of syntactic change will be greatly increased with the addition of accurate parse trees.

null

10.18653/v1/2022.findings-naacl.44

null

26,301

inproceedings

li-etal-2022-instilling

Instilling Type Knowledge in Language Models via Multi-Task {QA}

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.45/

Li, Shuyang and Sridhar, Mukund and Satya Prakash, Chandana and Cao, Jin and Hamza, Wael and McAuley, Julian

Findings of the Association for Computational Linguistics: NAACL 2022

594--603

Understanding human language often necessitates understanding entities and their place in a taxonomy of knowledge{---}their \textit{types}.Previous methods to learn entity types rely on training classifiers on datasets with coarse, noisy, and incomplete labels. We introduce a method to instill fine-grained type knowledge in language models with text-to-text pre-training on type-centric questions leveraging knowledge base documents and knowledge graphs.We create the \textbf{WikiWiki} dataset: entities and passages from 10M Wikipedia articles linked to the Wikidata knowledge graph with 41K types.Models trained on WikiWiki achieve state-of-the-art performance in zero-shot dialog state tracking benchmarks, accurately infer entity types in Wikipedia articles, and can discover new types deemed useful by human judges.

null

10.18653/v1/2022.findings-naacl.45

null

26,302

inproceedings

markowitz-etal-2022-statik

{S}t{ATIK}: Structure and Text for Inductive Knowledge Graph Completion

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.46/

Markowitz, Elan and Balasubramanian, Keshav and Mirtaheri, Mehrnoosh and Annavaram, Murali and Galstyan, Aram and Ver Steeg, Greg

Findings of the Association for Computational Linguistics: NAACL 2022

604--615

Knowledge graphs (KGs) often represent knowledge bases that are incomplete. Machine learning models can alleviate this by helping automate graph completion. Recently, there has been growing interest in completing knowledge bases that are dynamic, where previously unseen entities may be added to the KG with many missing links. In this paper, we present \textbf{StATIK}{--}\textbf{St}ructure \textbf{A}nd \textbf{T}ext for \textbf{I}nductive \textbf{K}nowledge Completion. StATIK uses Language Models to extract the semantic information from text descriptions, while using Message Passing Neural Networks to capture the structural information. StATIK achieves state of the art results on three challenging inductive baselines. We further analyze our hybrid model through detailed ablation studies.

null

10.18653/v1/2022.findings-naacl.46

null

26,303

inproceedings

nadejde-etal-2022-cocoa

{C}o{C}o{A}-{MT}: A Dataset and Benchmark for Contrastive Controlled {MT} with Application to Formality

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.47/

Nadejde, Maria and Currey, Anna and Hsu, Benjamin and Niu, Xing and Federico, Marcello and Dinu, Georgiana

Findings of the Association for Computational Linguistics: NAACL 2022

616--632

The machine translation (MT) task is typically formulated as that of returning a single translation for an input segment. However, in many cases, multiple different translations are valid and the appropriate translation may depend on the intended target audience, characteristics of the speaker, or even the relationship between speakers. Specific problems arise when dealing with honorifics, particularly translating from English into languages with formality markers. For example, the sentence {\textquotedblleft}Are you sure?{\textquotedblright} can be translated in German as {\textquotedblleft}Sind Sie sich sicher?{\textquotedblright} (formal register) or {\textquotedblleft}Bist du dir sicher?{\textquotedblright} (informal). Using wrong or inconsistent tone may be perceived as inappropriate or jarring for users of certain cultures and demographics. This work addresses the problem of learning to control target language attributes, in this case formality, from a small amount of labeled contrastive data. We introduce an annotated dataset (CoCoA-MT) and an associated evaluation metric for training and evaluating formality-controlled MT models for six diverse target languages. We show that we can train formality-controlled models by fine-tuning on labeled contrastive data, achieving high accuracy (82{\%} in-domain and 73{\%} out-of-domain) while maintaining overall quality.

null

10.18653/v1/2022.findings-naacl.47

null

26,304

inproceedings

li-etal-2022-clear

{CLEAR}: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.48/

Li, Jialu and Tan, Hao and Bansal, Mohit

Findings of the Association for Computational Linguistics: NAACL 2022

633--649

Vision-and-Language Navigation (VLN) tasks require an agent to navigate through the environment based on language instructions. In this paper, we aim to solve two key challenges in this task: utilizing multilingual instructions for improved instruction-path grounding and navigating through new environments that are unseen during training. To address these challenges, first, our agent learns a shared and visually-aligned cross-lingual language representation for the three languages (English, Hindi and Telugu) in the Room-Across-Room dataset. Our language representation learning is guided by text pairs that are aligned by visual information. Second, our agent learns an environment-agnostic visual representation by maximizing the similarity between semantically-aligned image pairs (with constraints on object-matching) from different environments. Our environment agnostic visual representation can mitigate the environment bias induced by low-level visual information. Empirically, on the Room-Across-Room dataset, we show that our multi-lingual agent gets large improvements in all metrics over the strong baseline model when generalizing to unseen environments with the cross-lingual language representation and the environment-agnostic visual representation. Furthermore, we show that our learned language and visual representations can be successfully transferred to the Room-to-Room and Cooperative Vision-and-Dialogue Navigation task, and present detailed qualitative and quantitative generalization and grounding analysis.

null

10.18653/v1/2022.findings-naacl.48

null

26,305

inproceedings

james-etal-2022-language

Language Models for Code-switch Detection of te reo {M}{\={a}}ori and {E}nglish in a Low-resource Setting

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.49/

James, Jesin and Yogarajan, Vithya and Shields, Isabella and Watson, Catherine and Keegan, Peter and Mahelona, Keoni and Jones, Peter-Lucas

Findings of the Association for Computational Linguistics: NAACL 2022

650--660

Te reo M{\={a}}ori, New Zealand`s only indigenous language, is code-switched with English. M{\={a}}ori speakers are atleast bilingual, and the use of M{\={a}}ori is increasing in New Zealand English. Unfortunately, due to the minimal availability of resources, including digital data, M{\={a}}ori is under-represented in technological advances. Cloud-based multilingual systems such as Google and Microsoft Azure support M{\={a}}ori language detection. However, we provide experimental evidence to show that the accuracy of such systems is low when detecting M{\={a}}ori. Hence, with the support of M{\={a}}ori community, we collect M{\={a}}ori and bilingual data to use natural language processing (NLP) to improve M{\={a}}ori language detection. We train bilingual sub-word embeddings and provide evidence to show that our bilingual embeddings improve overall accuracy compared to the publicly-available monolingual embeddings. This improvement has been verified for various NLP tasks using three bilingual databases containing formal transcripts and informal social media data. We also show that BiLSTM with pre-trained M{\={a}}ori-English sub-word embeddings outperforms large-scale contextual language models such as BERT on down streaming tasks of detecting M{\={a}}ori language. However, this research uses large models {\textquoteleft}as is' for transfer learning, where no further training was done on M{\={a}}ori-English data. The best accuracy of 87{\%} was obtained using BiLSTM with bilingual embeddings to detect M{\={a}}ori-English code-switching points.

null

10.18653/v1/2022.findings-naacl.49

null

26,306

inproceedings

chawla-etal-2022-opponent

Opponent Modeling in Negotiation Dialogues by Related Data Adaptation

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.50/

Chawla, Kushal and Lucas, Gale and May, Jonathan and Gratch, Jonathan

Findings of the Association for Computational Linguistics: NAACL 2022

661--674

Opponent modeling is the task of inferring another party`s mental state within the context of social interactions. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. A practical model for this task needs to infer these priorities of the opponent on the fly based on partial dialogues as input, without needing additional annotations for training. In this work, we propose a ranker for identifying these priorities from negotiation dialogues. The model takes in a partial dialogue as input and predicts the priority order of the opponent. We further devise ways to adapt related data sources for this task to provide more explicit supervision for incorporating the opponent`s preferences and offers, as a proxy to relying on granular utterance-level annotations. We show the utility of our proposed approach through extensive experiments based on two dialogue datasets. We find that the proposed data adaptations lead to strong performance in zero-shot and few-shot scenarios. Moreover, they allow the model to perform better than baselines while accessing fewer utterances from the opponent. We release our code to support future work in this direction.

null

10.18653/v1/2022.findings-naacl.50

null

26,307

inproceedings

zhao-etal-2022-lmturk

{LMT}urk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Framework

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.51/

Zhao, Mengjie and Mi, Fei and Wang, Yasheng and Li, Minglei and Jiang, Xin and Liu, Qun and Schuetze, Hinrich

Findings of the Association for Computational Linguistics: NAACL 2022

675--692

Vast efforts have been devoted to creating high-performance few-shot learners, i.e., large-scale pretrained language models (PLMs) that perform well with little downstream task training data. Training PLMs has incurred significant cost, but utilizing the few-shot learners is still challenging due to their enormous size. This work focuses on a crucial question: How to make effective use of these few-shot learners? We propose LMTurk, a novel approach that treats few-shotlearners as crowdsourcing workers. The rationale is that crowdsourcing workers are in fact few-shot learners: They are shown a few illustrative examples to learn about a task and then start annotating. LMTurk employs few-shot learners built upon PLMs as workers. We show that the resulting annotations can be utilized to train models that solve the task well and are small enough to be deployable in practical scenarios. Active learning is integrated into LMTurk to reduce the amount of queries made to PLMs, minimizing the computational cost of running PLM inference passes. Altogether, LMTurk is an important step towards making effective use of current PLMs.

null

10.18653/v1/2022.findings-naacl.51

null

26,308

inproceedings

onoe-etal-2022-entity

Entity Cloze By Date: What {LM}s Know About Unseen Entities

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.52/

Onoe, Yasumasa and Zhang, Michael and Choi, Eunsol and Durrett, Greg

Findings of the Association for Computational Linguistics: NAACL 2022

693--702

Language models (LMs) are typically trained once on a large-scale corpus and used for years without being updated. However, in a dynamic world, new entities constantly arise. We propose a framework to analyze what LMs can infer about new entities that did not exist when the LMs were pretrained. We derive a dataset of entities indexed by their origination date and paired with their English Wikipedia articles, from which we can find sentences about each entity. We evaluate LMs' perplexity on masked spans within these sentences. We show that models more informed about the entities, such as those with access to a textual definition of them, achieve lower perplexity on this benchmark. Our experimental results demonstrate that making inferences about new entities remains difficult for LMs. Given its wide coverage on entity knowledge and temporal indexing, our dataset can be used to evaluate LMs and techniques designed to modify or extend their knowledge. Our automatic data collection pipeline can be easily used to continually update our benchmark.

null

10.18653/v1/2022.findings-naacl.52

null

26,309

inproceedings

liu-etal-2022-data

Data Augmentation for Low-Resource Dialogue Summarization

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.53/

Liu, Yongtai and Maynez, Joshua and Sim{\~o}es, Gon{\c{c}}alo and Narayan, Shashi

Findings of the Association for Computational Linguistics: NAACL 2022

703--710

We present DADS, a novel Data Augmentation technique for low-resource Dialogue Summarization. Our method generates synthetic examples by replacing sections of text from both the input dialogue and summary while preserving the augmented summary to correspond to a viable summary for the augmented dialogue. We utilize pretrained language models that produce highly likely dialogue alternatives while still being free to generate diverse alternatives. We applied our data augmentation method to the SAMSum dataset in low resource scenarios, mimicking real world problems such as chat, thread, and meeting summarization where large scale supervised datasets with human-written summaries are scarce. Through both automatic and human evaluations, we show that DADS shows strong improvements for low resource scenarios while generating topically diverse summaries without introducing additional hallucinations to the summaries.

null

10.18653/v1/2022.findings-naacl.53

null

26,310

inproceedings

zhao-etal-2022-versatile

A Versatile Adaptive Curriculum Learning Framework for Task-oriented Dialogue Policy Learning

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.54/

Zhao, Yang and Qin, Hua and Zhenyu, Wang and Zhu, Changxi and Wang, Shihan

Findings of the Association for Computational Linguistics: NAACL 2022

711--723

Training a deep reinforcement learning-based dialogue policy with brute-force random sampling is costly. A new training paradigm was proposed to improve learning performance and efficiency by combining curriculum learning. However, attempts in the field of dialogue policy are very limited due to the lack of reliable evaluation of difficulty scores of dialogue tasks and the high sensitivity to the mode of progression through dialogue tasks. In this paper, we present a novel versatile adaptive curriculum learning (VACL) framework, which presents a substantial step toward applying automatic curriculum learning on dialogue policy tasks. It supports evaluating the difficulty of dialogue tasks only using the learning experiences of dialogue policy and skip-level selection according to their learning needs to maximize the learning efficiency. Moreover, an attractive feature of VACL is the construction of a generic, elastic global curriculum while training a good dialogue policy that could guide different dialogue policy learning without extra effort on re-training. The superiority and versatility of VACL are validated on three public dialogue datasets.

null

10.18653/v1/2022.findings-naacl.54

null

26,311

inproceedings

guo-etal-2022-longt5

{L}ong{T}5: {E}fficient Text-To-Text Transformer for Long Sequences

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.55/

Guo, Mandy and Ainslie, Joshua and Uthus, David and Ontanon, Santiago and Ni, Jianmo and Sung, Yun-Hsuan and Yang, Yinfei

Findings of the Association for Computational Linguistics: NAACL 2022

724--736

Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models. In this paper, we present LongT5, a new model that explores the effects of scaling both the input length and model size at the same time. Specifically, we integrate attention ideas from long-input transformers (ETC), and adopt pre-training strategies from summarization pre-training (PEGASUS) into the scalable T5 architecture. The result is a new attention mechanism we call Transient Global (TGlobal), which mimics ETC`s local/global attention mechanism, but without requiring additional side-inputs. We are able to achieve state-of-the-art results on several summarization and question answering tasks, as well as outperform the original T5 models on these tasks. We have open sourced our architecture and training code, as well as our pre-trained model checkpoints.

null

10.18653/v1/2022.findings-naacl.55

null

26,312

inproceedings

pokrywka-etal-2022-challenging

Challenging {A}merica: Modeling language in longer time scales

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.56/

Pokrywka, Jakub and Grali{\'n}ski, Filip and Jassem, Krzysztof and Kaczmarek, Karol and Jurkiewicz, Krzysztof and Wierzchon, Piotr

Findings of the Association for Computational Linguistics: NAACL 2022

737--749

The aim of the paper is to apply, for historical texts, the methodology used commonly to solve various NLP tasks defined for contemporary data, i.e. pre-train and fine-tune large Transformer models. This paper introduces an ML challenge, named Challenging America (ChallAm), based on OCR-ed excerpts from historical newspapers collected from the Chronicling America portal. ChallAm provides a dataset of clippings, labeled with metadata on their origin, and paired with their textual contents retrieved by an OCR tool. Three, publicly available, ML tasks are defined in the challenge: to determine the article date, to detect the location of the issue, and to deduce a word in a text gap (cloze test). Strong baselines are provided for all three ChallAm tasks. In particular, we pre-trained a RoBERTa model from scratch from the historical texts. We also discuss the issues of discrimination and hate-speech present in the historical American texts.

null

10.18653/v1/2022.findings-naacl.56

null

26,313

inproceedings

kaur-etal-2022-lm

{LM}-{CORE}: Language Models with Contextually Relevant External Knowledge

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.57/

Kaur, Jivat and Bhatia, Sumit and Aggarwal, Milan and Bansal, Rachit and Krishnamurthy, Balaji

Findings of the Association for Computational Linguistics: NAACL 2022

750--769

Large transformer-based pre-trained language models have achieved impressive performance on a variety of knowledge-intensive tasks and can capture factual knowledge in their parameters. We argue that storing large amounts of knowledge in the model parameters is sub-optimal given the ever-growing amounts of knowledge and resource requirements. We posit that a more efficient alternative is to provide explicit access to contextually relevant structured knowledge to the model and train it to use that knowledge. We present LM-CORE {--} a general framework to achieve this{--} that allows \textit{decoupling} of the language model training from the external knowledge source and allows the latter to be updated without affecting the already trained model. Experimental results show that LM-CORE, having access to external knowledge, achieves significant and robust outperformance over state-of-the-art knowledge-enhanced language models on knowledge probing tasks; can effectively handle knowledge updates; and performs well on two downstream tasks. We also present a thorough error analysis highlighting the successes and failures of LM-CORE. Our code and model checkpoints are publicly available.

null

10.18653/v1/2022.findings-naacl.57

null

26,314

inproceedings

hosseini-asl-etal-2022-generative

A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.58/

Hosseini-Asl, Ehsan and Liu, Wenhao and Xiong, Caiming

Findings of the Association for Computational Linguistics: NAACL 2022

770--787

Sentiment analysis is an important task in natural language processing. In recent works, pre-trained language models are often used to achieve state-of-the-art results, especially when training data is scarce. It is common to fine-tune on the downstream task, usually by adding task-specific layers on top of the model. In this paper, we focus on aspect-based sentiment analysis, which involves extracting aspect term, category, and predicting their corresponding polarities. In particular, we are interested in few-shot settings. We propose to reformulate the extraction and prediction tasks into the sequence generation task, using a generative language model with unidirectional attention (GPT2 is used unless stated otherwise). This way, the model learns to accomplish the tasks via language generation without the need of training task-specific layers. Our evaluation results on the single-task polarity prediction show that our approach outperforms the previous state-of-the-art (based on BERT) on average performance by a large margins in few-shot and full-shot settings. More importantly, our generative approach significantly reduces the model variance caused by low-resource data. We further demonstrate that the proposed generative language model can handle joint and multi-task settings, unlike previous work. We observe that the proposed sequence generation method achieves further improved performances on polarity prediction when the model is trained via joint and multi-task settings. Further evaluation on similar sentiment analysis datasets, SST-2, SST-5 and OOS intent detection validates the superiority and noise robustness of generative language model in few-shot settings.

null

10.18653/v1/2022.findings-naacl.58

null

26,315

inproceedings

dash-etal-2022-permutation

Permutation Invariant Strategy Using Transformer Encoders for Table Understanding

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.59/

Dash, Sarthak and Bagchi, Sugato and Mihindukulasooriya, Nandana and Gliozzo, Alfio

Findings of the Association for Computational Linguistics: NAACL 2022

788--800

Representing text in tables is essential for many business intelligence tasks such as semantic retrieval, data exploration and visualization, and question answering. Existing methods that leverage pretrained Transformer encoders range from a simple construction of pseudo-sentences by concatenating text across rows or columns to complex parameter-intensive models that encode table structure and require additional pretraining. In this work, we introduce a novel encoding strategy for Transformer encoders that preserves the critical property of permutation invariance across rows or columns. Unlike existing state-of-the-art methods for Table Understanding, our proposed approach does not require any additional pretraining and still substantially outperforms existing methods in almost all instances. We demonstrate the effectiveness of our proposed approach on three table interpretation tasks: column type annotation, relation extraction, and entity linking through extensive experiments on existing tabular datasets.

null

10.18653/v1/2022.findings-naacl.59

null

26,316

inproceedings

tedeschi-navigli-2022-multinerd

{M}ulti{NERD}: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.60/

Tedeschi, Simone and Navigli, Roberto

Findings of the Association for Computational Linguistics: NAACL 2022

801--812

Named Entity Recognition (NER) is the task of identifying named entities in texts and classifying them through specific semantic categories, a process which is crucial for a wide range of NLP applications. Current datasets for NER focus mainly on coarse-grained entity types, tend to consider a single textual genre and to cover a narrow set of languages, thus limiting the general applicability of NER systems. In this work, we design a new methodology for automatically producing NER annotations, and address the aforementioned limitations by introducing a novel dataset that covers 10 languages, 15 NER categories and 2 textual genres. We also introduce a manually-annotated test set, and extensively evaluate the quality of our novel dataset on both this new test set and standard benchmarks for NER.In addition, in our dataset, we include: i) disambiguation information to enable the development of multilingual entity linking systems, and ii) image URLs to encourage the creation of multimodal systems. We release our dataset at \url{https://github.com/Babelscape/multinerd}.

null

10.18653/v1/2022.findings-naacl.60

null

26,317

inproceedings

lee-etal-2022-learning

Learning to Embed Multi-Modal Contexts for Situated Conversational Agents

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.61/

Lee, Haeju and Kwon, Oh Joon and Choi, Yunseon and Park, Minho and Han, Ran and Kim, Yoonhyung and Kim, Jinhyeon and Lee, Youngjune and Shin, Haebin and Lee, Kangwook and Kim, Kee-Eung

Findings of the Association for Computational Linguistics: NAACL 2022

813--830

The Situated Interactive Multi-Modal Conversations (SIMMC) 2.0 aims to create virtual shopping assistants that can accept complex multi-modal inputs, i.e. visual appearances of objects and user utterances. It consists of four subtasks, multi-modal disambiguation (MM-Disamb), multi-modal coreference resolution (MM-Coref), multi-modal dialog state tracking (MM-DST), and response retrieval and generation. While many task-oriented dialog systems usually tackle each subtask separately, we propose a jointly learned multi-modal encoder-decoder that incorporates visual inputs and performs all four subtasks at once for efficiency. This approach won the MM-Coref and response retrieval subtasks and nominated runner-up for the remaining subtasks using a single unified model at the 10th Dialog Systems Technology Challenge (DSTC10), setting a high bar for the novel task of multi-modal task-oriented dialog systems.

null

10.18653/v1/2022.findings-naacl.61

null

26,318

inproceedings

gan-etal-2022-measuring

Measuring and Improving Compositional Generalization in Text-to-{SQL} via Component Alignment

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.62/

Gan, Yujian and Chen, Xinyun and Huang, Qiuping and Purver, Matthew

Findings of the Association for Computational Linguistics: NAACL 2022

831--843

In text-to-SQL tasks {---} as in much of NLP {---} \textit{compositional generalization} is a major challenge: neural networks struggle with compositional generalization where training and test distributions differ. However, most recent attempts to improve this are based on word-level synthetic data or specific dataset splits to generate compositional biases. In this work, we propose a clause-level compositional example generation method. We first split the sentences in the Spider text-to-SQL dataset into sub-sentences, annotating each sub-sentence with its corresponding SQL clause, resulting in a new dataset Spider-SS. We then construct a further dataset, Spider-CG, by composing Spider-SS sub-sentences in different combinations, to test the ability of models to generalize compositionally. Experiments show that existing models suffer significant performance degradation when evaluated on Spider-CG, even though every sub-sentence is seen during training. To deal with this problem, we modify a number of state-of-the-art models to train on the segmented data of Spider-SS, and we show that this method improves the generalization performance.

null

10.18653/v1/2022.findings-naacl.62

null

26,319

inproceedings

samad-etal-2022-empathetic

Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.63/

Samad, Azlaan Mustafa and Mishra, Kshitij and Firdaus, Mauajama and Ekbal, Asif

Findings of the Association for Computational Linguistics: NAACL 2022

844--856

Persuasion is an intricate process involving empathetic connection between two individuals. Plain persuasive responses may make a conversation non-engaging. Even the most well-intended and reasoned persuasive conversations can fall through in the absence of empathetic connection between the speaker and listener. In this paper, we propose a novel task of incorporating empathy when generating persuasive responses. We develop an empathetic persuasive dialogue system by fine-tuning a maximum likelihood Estimation (MLE)-based language model in a reinforcement learning (RL) framework. To design feedbacks for our RL-agent, we define an effective and efficient reward function considering consistency, repetitiveness, emotion and persuasion rewards to ensure consistency, non-repetitiveness, empathy and persuasiveness in the generated responses. Due to lack of emotion annotated persuasive data, we first annotate the existing Persuaion For Good dataset with emotions, then build transformer based classifiers to provide emotion based feedbacks to our RL agent. Experimental results confirm that our proposed model increases the rate of generating persuasive responses as compared to the available state-of-the-art dialogue models while making the dialogues empathetically more engaging and retaining the language quality in responses.

null

10.18653/v1/2022.findings-naacl.63

null

26,320

inproceedings

cao-etal-2022-attention

Attention Fusion: a light yet efficient late fusion mechanism for task adaptation in {NLU}

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.64/

Cao, Jin and Satya Prakash, Chandana and Hamza, Wael

Findings of the Association for Computational Linguistics: NAACL 2022

857--866

Fine-tuning a pre-trained language model using annotated data has become the de-facto standard for adapting general-purpose pre-trained models like BERT to downstream tasks. However, given the trend of larger pre-trained models, fine-tuning these models for each downstream task is parameter-inefficient and computationally-expensive deeming this approach sub-optimal for adoption by NLU systems. In recent years, various approaches have been proposed for parameter efficient task adaptation such as Adaptor, Bitfit, Prompt tuning, Prefix tuning etc. However, most of these efforts propose to insert task specific parameters in-between or inside intermediate layers of the pre-trained encoder resulting in higher computational cost due to back-propagation of errors to all layers. To mitigate this issue, we propose a light but efficient, attention based fusion module which computes task-attuned token representations by aggregating intermediate layer representations from a pre-trained network. Our proposed fusion module trains only 0.0009{\%} of total parameters and achieves competitive performance to the standard fine-tuning approach on various tasks. It is also decoupled from the pre-trained network making it efficient during computation and scalable during deployment. Last but not the least, we demonstrate that our proposed attention-fusion mechanism can transfer effectively to different languages for further re-use and expansion.

null

10.18653/v1/2022.findings-naacl.64

null

26,321

inproceedings

mattern-etal-2022-limits

The Limits of Word Level Differential Privacy

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.65/

Mattern, Justus and Weggenmann, Benjamin and Kerschbaum, Florian

Findings of the Association for Computational Linguistics: NAACL 2022

867--881

As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data. A significant subset of these approaches incorporate differentially private mechanims to perturb word embeddings, thus replacing individual words in a sentence. While these methods represent very important contributions, have various advantages over other techniques and do show anonymization capabilities,they have several shortcomings. In this paper, we investigate these weaknesses and demonstrate significant mathematical constraints diminishing the theoretical privacy guaranteeas well as major practical shortcomings with regard to the protection against deanonymization attacks, the preservation of content of the original sentences as well as the quality of the language output. Finally, we propose a new method for text anonymization based on transformer based language models fine-tuned for paraphrasing that circumvents most of the identified weaknesses and also offers a formal privacy guarantee. We evaluate the performance of our method via thourough experimentation and demonstrate superior performance over the discussed mechanisms.

null

10.18653/v1/2022.findings-naacl.65

null

26,322

inproceedings

papadopoulos-etal-2022-efficient

Efficient Learning of Multiple {NLP} Tasks via Collective Weight Factorization on {BERT}

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.66/

Papadopoulos, Christos and Panagakis, Yannis and Koubarakis, Manolis and Nicolaou, Mihalis

Findings of the Association for Computational Linguistics: NAACL 2022

882--890

The Transformer architecture continues to show remarkable performance gains in many Natural Language Processing tasks. However, obtaining such state-of-the-art performance in different tasks requires fine-tuning the same model separately for each task. Clearly, such an approach is demanding in terms of both memory requirements and computing power. In this paper, aiming to improve training efficiency across multiple tasks, we propose to collectively factorize the weighs of the multi-head attention module of a pre-trained Transformer. We test our proposed method on finetuning multiple natural language understanding tasks by employing BERT-Large as an instantiation of the Transformer and the GLUE as the evaluation benchmark. Experimental results show that our method requires training and storing only 1{\%} of the initial model parameters for each task and matches or improves the original fine-tuned model`s performance for each task while effectively decreasing the parameter requirements by two orders of magnitude. Furthermore, compared to well-known adapter-based alternatives on the GLUE benchmark, our method consistently reaches the same levels of performance while requiring approximately four times fewer total and trainable parameters per task.

null

10.18653/v1/2022.findings-naacl.66

null

26,323

inproceedings

kulkarni-etal-2022-learning

Learning Rich Representation of Keyphrases from Text

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.67/

Kulkarni, Mayank and Mahata, Debanjan and Arora, Ravneet and Bhowmik, Rajarshi

Findings of the Association for Computational Linguistics: NAACL 2022

891--906

In this work, we explore how to train task-specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (upto 8.16 points in F1) over SOTA, when the LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction. In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases related to the input text in the CatSeq format, instead of the denoised original input. This also led to gains in performance (upto 4.33 points in F1@M) over SOTA for keyphrase generation. Additionally, we also fine-tune the pre-trained language models on named entity recognition (NER), question answering (QA), relation extraction (RE), abstractive summarization and achieve comparable performance with that of the SOTA, showing that learning rich representation of keyphrases is indeed beneficial for many other fundamental NLP tasks.

null

10.18653/v1/2022.findings-naacl.67

null

26,324

inproceedings

lin-etal-2022-improving

Improving Contextual Representation with Gloss Regularized Pre-training

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.68/

Lin, Yu and An, Zhecheng and Wu, Peihao and Ma, Zejun

Findings of the Association for Computational Linguistics: NAACL 2022

907--920

Though achieving impressive results on many NLP tasks, the BERT-like masked language models (MLM) encounter the discrepancy between pre-training and inference. In light of this gap, we investigate the contextual representation of pre-training and inference from the perspective of word probability distribution. We discover that BERT risks neglecting the contextual word similarity in pre-training. To tackle this issue, we propose an auxiliary gloss regularizer module to BERT pre-training (GR-BERT), to enhance word semantic similarity. By predicting masked words and aligning contextual embeddings to corresponding glosses simultaneously, the word similarity can be explicitly modeled. We design two architectures for GR-BERT and evaluate our model in downstream tasks. Experimental results show that the gloss regularizer benefits BERT in word-level and sentence-level semantic representation. The GR-BERT achieves new state-of-the-art in lexical substitution task and greatly promotes BERT sentence representation in both unsupervised and supervised STS tasks.

null

10.18653/v1/2022.findings-naacl.68

null

26,325

inproceedings

steinborn-etal-2022-information

An Information-Theoretic Approach and Dataset for Probing Gender Stereotypes in Multilingual Masked Language Models

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.69/

Steinborn, Victor and Dufter, Philipp and Jabbar, Haris and Schuetze, Hinrich

Findings of the Association for Computational Linguistics: NAACL 2022

921--932

Bias research in NLP is a rapidly growing and developing field. Similar to CrowS-Pairs (Nangia et al., 2020), we assess gender bias in masked-language models (MLMs) by studying pairs of sentences with gender swapped person references. Most bias research focuses on and often is specific to English.Using a novel methodology for creating sentence pairs that is applicable across languages, we create, based on CrowS-Pairs, a multilingual dataset for English, Finnish, German, Indonesian and Thai.Additionally, we propose $S_{JSD}$, a new bias measure based on Jensen{--}Shannon divergence, which we argue retains more information from the model output probabilities than other previously proposed bias measures for MLMs.Using multilingual MLMs, we find that $S_{JSD}$ diagnoses the same systematic biased behavior for non-English that previous studies have found for monolingual English pre-trained MLMs. $S_{JSD}$ outperforms the CrowS-Pairs measure, which struggles to find such biases for smaller non-English datasets.

null

10.18653/v1/2022.findings-naacl.69

null

26,326

inproceedings

zuo-etal-2022-self

Self-Training with Differentiable Teacher

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.70/

Zuo, Simiao and Yu, Yue and Liang, Chen and Jiang, Haoming and Er, Siawpeng and Zhang, Chao and Zhao, Tuo and Zha, Hongyuan

Findings of the Association for Computational Linguistics: NAACL 2022

933--949

Self-training achieves enormous success in various semi-supervised and weakly-supervised learning tasks. The method can be interpreted as a teacher-student framework, where the teacher generates pseudo-labels, and the student makes predictions. The two models are updated alternatingly. However, such a straightforward alternating update rule leads to training instability. This is because a small change in the teacher may result in a significant change in the student. To address this issue, we propose DRIFT, short for differentiable self-training, that treats teacher-student as a Stackelberg game. In this game, a leader is always in a more advantageous position than a follower. In self-training, the student contributes to the prediction performance, and the teacher controls the training process by generating pseudo-labels. Therefore, we treat the student as the leader and the teacher as the follower. The leader procures its advantage by acknowledging the follower`s strategy, which involves differentiable pseudo-labels and differentiable sample weights. Consequently, the leader-follower interaction can be effectively captured via Stackelberg gradient, obtained by differentiating the follower`s strategy. Experimental results on semi- and weakly-supervised classification and named entity recognition tasks show that our model outperforms existing approaches by large margins.

null

10.18653/v1/2022.findings-naacl.70

null

26,327

inproceedings

zhang-etal-2022-sharp

{SHARP}: Search-Based Adversarial Attack for Structured Prediction

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.71/

Zhang, Liwen and Jia, Zixia and Han, Wenjuan and Zheng, Zilong and Tu, Kewei

Findings of the Association for Computational Linguistics: NAACL 2022

950--961

Adversarial attack of structured prediction models faces various challenges such as the difficulty of perturbing discrete words, the sentence quality issue, and the sensitivity of outputs to small perturbations. In this work, we introduce SHARP, a new attack method that formulates the black-box adversarial attack as a search-based optimization problem with a specially designed objective function considering sentence fluency, meaning preservation and attacking effectiveness. Additionally, three different searching strategies are analyzed and compared, i.e., Beam Search, Metropolis-Hastings Sampling, and Hybrid Search. We demonstrate the effectiveness of our attacking strategies on two challenging structured prediction tasks: Pos-tagging and dependency parsing. Through automatic and human evaluations, we show that our method performs a more potent attack compared with pioneer arts. Moreover, the generated adversarial examples can be used to successfully boost the robustness and performance of the victim model via adversarial training.

null

10.18653/v1/2022.findings-naacl.71

null

26,328

inproceedings

cheema-etal-2022-mm

{MM}-Claims: A Dataset for Multimodal Claim Detection in Social Media

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.72/

Cheema, Gullal Singh and Hakimov, Sherzod and Sittar, Abdul and M{\"uller-Budack, Eric and Otto, Christian and Ewerth, Ralph

Findings of the Association for Computational Linguistics: NAACL 2022

962--979

In recent years, the problem of misinformation on the web has become widespread across languages, countries, and various social media platforms. Although there has been much work on automated fake news detection, the role of images and their variety are not well explored. In this paper, we investigate the roles of image and text at an earlier stage of the fake news detection pipeline, called claim detection. For this purpose, we introduce a novel dataset, MM-Claims, which consists of tweets and corresponding images over three topics: COVID-19, Climate Change and broadly Technology. The dataset contains roughly 86000 tweets, out of which 3400 are labeled manually by multiple annotators for the training and evaluation of multimodal models. We describe the dataset in detail, evaluate strong unimodal and multimodal baselines, and analyze the potential and drawbacks of current models.

null

10.18653/v1/2022.findings-naacl.72

null

26,329

inproceedings

li-sogaard-2022-qlevr

{QLEVR}: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoning

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.73/

Li, Zechen and S{\o}gaard, Anders

Findings of the Association for Computational Linguistics: NAACL 2022

980--996

Synthetic datasets have successfully been used to probe visual question-answering datasets for their reasoning abilities. CLEVR (John- son et al., 2017), for example, tests a range of visual reasoning abilities. The questions in CLEVR focus on comparisons of shapes, colors, and sizes, numerical reasoning, and existence claims. This paper introduces a minimally biased, diagnostic visual question-answering dataset, QLEVR, that goes beyond existential and numerical quantification and focus on more complex quantifiers and their combinations, e.g., asking whether there are more than two red balls that are smaller than at least three blue balls in an image. We describe how the dataset was created and present a first evaluation of state-of-the-art visual question-answering models, showing that QLEVR presents a formidable challenge to our current models. Code and Dataset are available at \url{https://github.com/zechenli03/QLEVR}

null

10.18653/v1/2022.findings-naacl.73

null

26,330

inproceedings

liang-etal-2022-mwp

{MWP}-{BERT}: Numeracy-Augmented Pre-training for Math Word Problem Solving

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.74/

Liang, Zhenwen and Zhang, Jipeng and Wang, Lei and Qin, Wei and Lan, Yunshi and Shao, Jie and Zhang, Xiangliang

Findings of the Association for Computational Linguistics: NAACL 2022

997--1009

Math word problem (MWP) solving faces a dilemma in number representation learning. In order to avoid the number representation issue and reduce the search space of feasible solutions, existing works striving for MWP solving usually replace real numbers with symbolic placeholders to focus on logic reasoning. However, different from common symbolic reasoning tasks like program synthesis and knowledge graph reasoning, MWP solving has extra requirements in numerical reasoning. In other words, instead of the number value itself, it is the reusable numerical property that matters more in numerical reasoning. Therefore, we argue that injecting numerical properties into symbolic placeholders with contextualized representation learning schema can provide a way out of the dilemma in the number representation issue here. In this work, we introduce this idea to the popular pre-training language model (PLM) techniques and build MWP-BERT, an effective contextual number representation PLM. We demonstrate the effectiveness of our MWP-BERT on MWP solving and several MWP-specific understanding tasks on both English and Chinese benchmarks.

null

10.18653/v1/2022.findings-naacl.74

null

26,331

inproceedings

gershuni-pinter-2022-restoring

Restoring {H}ebrew Diacritics Without a Dictionary

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.75/

Gershuni, Elazar and Pinter, Yuval

Findings of the Association for Computational Linguistics: NAACL 2022

1010--1018

We demonstrate that it is feasible to accurately diacritize Hebrew script without any human-curated resources other than plain diacritized text. We present Nakdimon, a two-layer character-level LSTM, that performs on par with much more complicated curation-dependent systems, across a diverse array of modern Hebrew sources. The model is accompanied by a training set and a test set, collected from diverse sources.

null

10.18653/v1/2022.findings-naacl.75

null

26,332

inproceedings

lee-etal-2022-masked

Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.76/

Lee, Hwanhee and Yoo, Kang Min and Park, Joonsuk and Lee, Hwaran and Jung, Kyomin

Findings of the Association for Computational Linguistics: NAACL 2022

1019--1030

Despite the recent advances in abstractive summarization systems, it is still difficult to determine whether a generated summary is factual consistent with the source text. To this end, the latest approach is to train a factual consistency classifier on factually consistent and inconsistent summaries. Luckily, the former is readily available as reference summaries in existing summarization datasets. However, generating the latter remains a challenge, as they need to be factually inconsistent, yet closely relevant to the source text to be effective. In this paper, we propose to generate factually inconsistent summaries using source texts and reference summaries with key information masked. Experiments on seven benchmark datasets demonstrate that factual consistency classifiers trained on summaries generated using our method generally outperform existing models and show a competitive correlation with human judgments. We also analyze the characteristics of the summaries generated using our method. We will release the pre-trained model and the code at \url{https://github.com/hwanheelee1993/MFMA}.

null

10.18653/v1/2022.findings-naacl.76

null

26,333

inproceedings

rosch-libovicky-2022-probing

Probing the Role of Positional Information in Vision-Language Models

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.77/

R{\"osch, Philipp J. and Libovick{\'y, Jind{\v{rich

Findings of the Association for Computational Linguistics: NAACL 2022

1031--1041

In most Vision-Language models (VL), the understanding of the image structure is enabled by injecting the position information (PI) about objects in the image. In our case study of LXMERT, a state-of-the-art VL model, we probe the use of the PI in the representation and study its effect on Visual Question Answering. We show that the model is not capable of leveraging the PI for the image-text matching task on a challenge set where only position differs. Yet, our experiments with probing confirm that the PI is indeed present in the representation. We introduce two strategies to tackle this: (i) Positional Information Pre-training and (ii) Contrastive Learning on PI using Cross-Modality Matching. Doing so, the model can correctly classify if images with detailed PI statements match. Additionally to the 2D information from bounding boxes, we introduce the object`s depth as new feature for a better object localization in the space. Even though we were able to improve the model properties as defined by our probes, it only has a negligible effect on the downstream performance. Our results thus highlight an important issue of multimodal modeling: the mere presence of information detectable by a probing classifier is not a guarantee that the information is available in a cross-modal setup.

null

10.18653/v1/2022.findings-naacl.77

null

26,334

inproceedings

kumar-etal-2022-diversity

{\textquotedblright}Diversity and Uncertainty in Moderation{\textquotedblright} are the Key to Data Selection for Multilingual Few-shot Transfer

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.78/

Kumar, Shanu and Dandapat, Sandipan and Choudhury, Monojit

Findings of the Association for Computational Linguistics: NAACL 2022

1042--1055

Few-shot transfer often shows substantial gain over zero-shot transfer (CITATION), which is a practically useful trade-off between fully supervised and unsupervised learning approaches for multilingual pretained model-based systems. This paper explores various strategies for selecting data for annotation that can result in a better few-shot transfer. The proposed approaches rely on multiple measures such as data entropy using $n$-gram language model, predictive entropy, and gradient embedding. We propose a loss embedding method for sequence labeling tasks, which induces diversity and uncertainty sampling similar to gradient embedding. The proposed data selection strategies are evaluated and compared for POS tagging, NER, and NLI tasks for up to 20 languages. Our experiments show that the gradient and loss embedding-based strategies consistently outperform random data selection baselines, with gains varying with the initial performance of the zero-shot transfer. Furthermore, the proposed method shows similar trends in improvement even when the model is fine-tuned using a lower proportion of the original task-specific labeled training data for zero-shot transfer.

null

10.18653/v1/2022.findings-naacl.78

null

26,335

inproceedings

zhu-etal-2022-self

A Self-supervised Joint Training Framework for Document Reranking

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.79/

Zhu, Xiaozhi and Hao, Tianyong and Cheng, Sijie and Wang, Fu Lee and Liu, Hai

Findings of the Association for Computational Linguistics: NAACL 2022

1056--1065

Pretrained language models such as BERT have been successfully applied to a wide range of natural language processing tasks and also achieved impressive performance in document reranking tasks. Recent works indicate that further pretraining the language models on the task-specific datasets before fine-tuning helps improve reranking performance. However, the pre-training tasks like masked language model and next sentence prediction were based on the context of documents instead of encouraging the model to understand the content of queries in document reranking task. In this paper, we propose a new self-supervised joint training framework (SJTF) with a self-supervised method called Masked Query Prediction (MQP) to establish semantic relations between given queries and positive documents. The framework randomly masks a token of query and encodes the masked query paired with positive documents, and uses a linear layer as a decoder to predict the masked token. In addition, the MQP is used to jointly optimize the models with supervised ranking objective during fine-tuning stage without an extra further pre-training stage. Extensive experiments on the MS MARCO passage ranking and TREC Robust datasets show that models trained with our framework obtain significant improvements compared to original models.

null

10.18653/v1/2022.findings-naacl.79

null

26,336

inproceedings

wang-etal-2022-code

{CODE}-{MVP}: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.80/

Wang, Xin and Wang, Yasheng and Wan, Yao and Wang, Jiawei and Zhou, Pingyi and Li, Li and Wu, Hao and Liu, Jin

Findings of the Association for Computational Linguistics: NAACL 2022

1066--1077

Recent years have witnessed increasing interest in code representation learning, which aims to represent the semantics of source code into distributed vectors. Currently, various works have been proposed to represent the complex semantics of source code from different views, including plain text, Abstract Syntax Tree (AST), and several kinds of code graphs (e.g., Control/Data Flow Graph). However, most of them only consider a single view of source code independently, ignoring the correspondences among different views. In this paper, we propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training, and name our model as CODE-MVP. Specifically, we first extract multiple code views using compiler tools, and learn the complementary information among them under a contrastive learning framework. Inspired by the type checking in compilation, we also design a fine-grained type inference objective in the pre-training. Experiments on three downstream tasks over five datasets demonstrate the superiority of CODE-MVP when compared with several state-of-the-art baselines. For example, we achieve 2.4/2.3/1.1 gain in terms of MRR/MAP/Accuracy metrics on natural language code retrieval, code similarity, and code defect detection tasks, respectively.

null

10.18653/v1/2022.findings-naacl.80

null

26,337

inproceedings

wang-etal-2022-rgl

{RGL}: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.81/

Wang, Yaqing and Tian, Xin and Xiong, Haoyi and Li, Yueyang and Chen, Zeyu and Guo, Sheng and Dou, Dejing

Findings of the Association for Computational Linguistics: NAACL 2022

1078--1084

Pre-trained language models (PLMs) can provide a good starting point for downstream applications. However, it is difficult to generalize PLMs to new tasks given a few labeled samples. In this work, we show that Relation Graph augmented Learning (RGL) can improve the performance of few-shot natural language understanding tasks. During learning, RGL constructs a relation graph based on the label consistency between samples in the same batch, and learns to solve the resultant node classification and link prediction problems on the relation graph. In this way, RGL fully exploits the limited supervised information, which can boost the tuning effectiveness. Extensive experimental results show that RGL consistently improves the performance of prompt-based tuning strategies.

null

10.18653/v1/2022.findings-naacl.81

null

26,338

inproceedings

jpliu-wtu-edu-cn-etal-2022-seeing

Seeing the wood for the trees: a contrastive regularization method for the low-resource Knowledge Base Question Answering

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.82/

[email protected], [email protected] and Mei, Shijie and Hu, Xinrong and Yao, Xun and Yang, Jack and Guo, Yi

Findings of the Association for Computational Linguistics: NAACL 2022

1085--1094

Given a context knowledge base (KB) and a corresponding question, the Knowledge Base Question Answering task aims to retrieve correct answer entities from this KB. Despite sophisticated retrieval algorithms, the impact of the low-resource (incomplete) KB is not fully exploited, where contributing components (. key entities and/or relations) may be absent for question answering. To effectively address this problem, we propose a contrastive regularization based method, which is motivated by the learn-by-analogy capability from human readers. Specifically, the proposed work includes two major modules: the knowledge extension and sMoCo module. The former aims at exploiting the latent knowledge from the context KB and generating auxiliary information in the form of question-answer pairs. The later module utilizes those additional pairs and applies the contrastive regularization to learn informative representations, that making hard positive pairs attracted and hard negative pairs separated. Empirically, we achieved the state-of-the-art performance on the WebQuestionsSP dataset and the effectiveness of proposed modules is also evaluated.

null

10.18653/v1/2022.findings-naacl.82

null

26,339

inproceedings

lei-etal-2022-phrase

Phrase-level Textual Adversarial Attack with Label Preservation

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.83/

Lei, Yibin and Cao, Yu and Li, Dianqi and Zhou, Tianyi and Fang, Meng and Pechenizkiy, Mykola

Findings of the Association for Computational Linguistics: NAACL 2022

1095--1112

Generating high-quality textual adversarial examples is critical for investigating the pitfalls of natural language processing (NLP) models and further promoting their robustness. Existing attacks are usually realized through word-level or sentence-level perturbations, which either limit the perturbation space or sacrifice fluency and textual quality, both affecting the attack effectiveness. In this paper, we propose Phrase-Level Textual Adversarial ATtack (PLAT) that generates adversarial samples through phrase-level perturbations. PLAT first extracts the vulnerable phrases as attack targets by a syntactic parser, and then perturbs them by a pre-trained blank-infilling model. Such flexible perturbation design substantially expands the search space for more effective attacks without introducing too many modifications, and meanwhile maintaining the textual fluency and grammaticality via contextualized generation using surrounding texts. Moreover, we develop a label preservation filter leveraging the likelihoods of language models fine-tuned on each class, rather than textual similarity, to rule out those perturbations that potentially alter the original class label for humans. Extensive experiments and human evaluation demonstrate that PLAT has a superior attack effectiveness as well as a better label consistency than strong baselines.

null

10.18653/v1/2022.findings-naacl.83

null

26,340

inproceedings

varshney-etal-2022-prompt

Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.84/

Varshney, Vaibhav and Patidar, Mayur and Kumar, Rajat and Vig, Lovekesh and Shroff, Gautam

Findings of the Association for Computational Linguistics: NAACL 2022

1113--1127

Identifying all possible user intents for a dialog system at design time is challenging even for skilled domain experts. For practical applications, novel intents may have to be inferred incrementally on the fly. This typically entails repeated retraining of the intent detector on both the existing and novel intents which can be expensive and would require storage of all past data corresponding to prior intents. In this paper, the objective is to continually train an intent detector on new intents while maintaining performance on prior intents without mandating access to prior intent data. Several data replay-based approaches have been introduced to avoid catastrophic forgetting during continual learning, including exemplar and generative replay. Current generative replay approaches struggle to generate representative samples because the generation is conditioned solely on the class/task label. Motivated by the recent work around prompt-based generation via pre-trained language models (PLMs), we employ generative replay using PLMs for incremental intent detection. Unlike exemplar replay, we only store the relevant contexts per intent in memory and use these stored contexts (with the class label) as prompts for generating intent-specific utterances. We use a common model for both generation and classification to promote optimal sharing of knowledge across both tasks. To further improve generation, we employ supervised contrastive fine-tuning of the PLM. Our proposed approach achieves state-of-the-art (SOTA) for lifelong intent detection on four public datasets and even outperforms exemplar replay-based approaches. The technique also achieves SOTA on a lifelong relation extraction task, suggesting that the approach is extendable to other continual learning tasks beyond intent detection.

null

10.18653/v1/2022.findings-naacl.84

null

26,341

inproceedings

tang-etal-2022-otextsum

{OTE}xt{S}um: {E}xtractive {T}ext {S}ummarisation with {O}ptimal {T}ransport

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.85/

Tang, Peggy and Hu, Kun and Yan, Rui and Zhang, Lei and Gao, Junbin and Wang, Zhiyong

Findings of the Association for Computational Linguistics: NAACL 2022

1128--1141

Extractive text summarisation aims to select salient sentences from a document to form a short yet informative summary. While learning-based methods have achieved promising results, they have several limitations, such as dependence on expensive training and lack of interpretability. Therefore, in this paper, we propose a novel non-learning-based method by for the first time formulating text summarisation as an Optimal Transport (OT) problem, namely Optimal Transport Extractive Summariser (OTExtSum). Optimal sentence extraction is conceptualised as obtaining an optimal summary that minimises the transportation cost to a given document regarding their semantic distributions. Such a cost is defined by the Wasserstein distance and used to measure the summary`s semantic coverage of the original document. Comprehensive experiments on four challenging and widely used datasets - MultiNews, PubMed, BillSum, and CNN/DM demonstrate that our proposed method outperforms the state-of-the-art non-learning-based methods and several recent learning-based methods in terms of the ROUGE metric.

null

10.18653/v1/2022.findings-naacl.85

null

26,342

inproceedings

tezekbayev-etal-2022-speeding

Speeding Up Entmax

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.86/

Tezekbayev, Maxat and Nikoulina, Vassilina and Gall{\'e}, Matthias and Assylbekov, Zhenisbek

Findings of the Association for Computational Linguistics: NAACL 2022

1142--1158

Softmax is the de facto standard for normalizing logits in modern neural networks for language processing. However, by producing a dense probability distribution each token in the vocabulary has a nonzero chance of being selected at each generation step, leading to a variety of reported problems in text generation. $\alpha$-entmax of Peters et al. (2019) solves this problem, but is unfortunately slower than softmax. In this paper, we propose an alternative to $\alpha$-entmax, which keeps its virtuous characteristics, but is as fast as optimized softmax and achieves on par or better performance in machine translation task.

null

10.18653/v1/2022.findings-naacl.86

null

26,343

inproceedings

ozates-etal-2022-improving

Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.87/

{\"Ozate{\c{s, {\c{Saziye Bet{\"ul and {\"Ozg{\"ur, Arzucan and Gungor, Tunga and {\c{Cetino{\u{glu, {\"Ozlem

Findings of the Association for Computational Linguistics: NAACL 2022

1159--1171

Code-switching dependency parsing stands as a challenging task due to both the scarcity of necessary resources and the structural difficulties embedded in code-switched languages. In this study, we introduce novel sequence labeling models to be used as auxiliary tasks for dependency parsing of code-switched text in a semi-supervised scheme. We show that using auxiliary tasks enhances the performance of an LSTM-based dependency parsing model and leads to better results compared to an XLM-R-based model with significantly less computational and time complexity. As the first study that focuses on multiple code-switching language pairs for dependency parsing, we acquire state-of-the-art scores on all of the studied languages. Our best models outperform the previous work by 7.4 LAS points on average.

null

10.18653/v1/2022.findings-naacl.87

null

26,344

inproceedings

liu-etal-2022-dangling

Dangling-Aware Entity Alignment with Mixed High-Order Proximities

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.88/

Liu, Juncheng and Sun, Zequn and Hooi, Bryan and Wang, Yiwei and Liu, Dayiheng and Yang, Baosong and Xiao, Xiaokui and Chen, Muhao

Findings of the Association for Computational Linguistics: NAACL 2022

1172--1184

We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of entities, a KG commonly contains some dangling entities that cannot find counterparts in other KGs. Therefore, dangling-aware entity alignment is more realistic than the conventional entity alignment where prior studies simply ignore dangling entities. We propose a framework using mixed high-order proximities on dangling-aware entity alignment. Our framework utilizes both the local high-order proximity in a nearest neighbor subgraph and the global high-order proximity in an embedding space for both dangling detection and entity alignment. Extensive experiments with two evaluation settings shows that our method more precisely detects dangling entities, and better aligns matchable entities. Further investigations demonstrate that our framework can mitigate the hubness problem on dangling-aware entity alignment.

null

10.18653/v1/2022.findings-naacl.88

null

26,345

inproceedings

luo-etal-2022-decbert

{D}ec{BERT}: Enhancing the Language Understanding of {BERT} with Causal Attention Masks

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.89/

Luo, Ziyang and Xi, Yadong and Ma, Jing and Yang, Zhiwei and Mao, Xiaoxi and Fan, Changjie and Zhang, Rongsheng

Findings of the Association for Computational Linguistics: NAACL 2022

1185--1197

Since 2017, the Transformer-based models play critical roles in various downstream Natural Language Processing tasks. However, a common limitation of the attention mechanism utilized in Transformer Encoder is that it cannot automatically capture the information of word order, so explicit position embeddings are generally required to be fed into the target model. In contrast, Transformer Decoder with the causal attention masks is naturally sensitive to the word order. In this work, we focus on improving the position encoding ability of BERT with the causal attention masks. Furthermore, we propose a new pre-trained language model \textit{DecBERT} and evaluate it on the GLUE benchmark. Experimental results show that (1) the causal attention mask is effective for BERT on the language understanding tasks; (2) our \textit{DecBERT} model without position embeddings achieve comparable performance on the GLUE benchmark; and (3) our modification accelerates the pre-training process and \textit{DecBERT w/ PE} achieves better overall performance than the baseline systems when pre-training with the same amount of computational resources.

null

10.18653/v1/2022.findings-naacl.89

null

26,346

inproceedings

tsvigun-etal-2022-towards

Towards Computationally Feasible Deep Active Learning

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.90/

Tsvigun, Akim and Shelmanov, Artem and Kuzmin, Gleb and Sanochkin, Leonid and Larionov, Daniil and Gusev, Gleb and Avetisian, Manvel and Zhukov, Leonid

Findings of the Association for Computational Linguistics: NAACL 2022

1198--1218

Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models. Deep learning offers a solution for several essential obstacles to deploying AL in practice but introduces many others. One of such problems is the excessive computational resources required to train an acquisition model and estimate its uncertainty on instances in the unlabeled pool. We propose two techniques that tackle this issue for text classification and tagging tasks, offering a substantial reduction of AL iteration duration and the computational overhead introduced by deep acquisition models in AL. We also demonstrate that our algorithm that leverages pseudo-labeling and distilled models overcomes one of the essential obstacles revealed previously in the literature. Namely, it was shown that due to differences between an acquisition model used to select instances during AL and a successor model trained on the labeled data, the benefits of AL can diminish. We show that our algorithm, despite using a smaller and faster acquisition model, is capable of training a more expressive successor model with higher performance.

null

10.18653/v1/2022.findings-naacl.90

null

26,347

inproceedings

you-etal-2022-end

End-to-end Spoken Conversational Question Answering: Task, Dataset and Model

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.91/

You, Chenyu and Chen, Nuo and Liu, Fenglin and Ge, Shen and Wu, Xian and Zou, Yuexian

Findings of the Association for Computational Linguistics: NAACL 2022

1219--1232

In spoken question answering, the systems are designed to answer questions from contiguous text spans within the related speech transcripts. However, the most natural way that human seek or test their knowledge is via human conversations. Therefore, we propose a new Spoken Conversational Question Answering task (SCQA), aiming at enabling the systems to model complex dialogues flow given the speech documents. In this task, our main objective is to build the system to deal with conversational questions based on the audio recordings, and to explore the plausibility of providing more cues from different modalities with systems in information gathering. To this end, instead of directly adopting automatically generated speech transcripts with highly noisy data, we propose a novel unified data distillation approach, DDNet, which effectively ingests cross-modal information to achieve fine-grained representations of the speech and language modalities. Moreover, we propose a simple and novel mechanism, termed Dual Attention, by encouraging better alignments between audio and text to ease the process of knowledge transfer. To evaluate the capacity of SCQA systems in a dialogue-style interaction, we assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with more than 40k question-answer pairs from 4k conversations. We first show that the performance of the existing state-of-the-art methods significantly degrade on our dataset, hence demonstrating the necessity of incorporating cross-modal information to achieve good performance gains. Our experimental results demonstrate that our proposed method achieves superior performance in spoken conversational question answering. Codes and datasets will be made publicly available.

null

10.18653/v1/2022.findings-naacl.91

null

26,348

inproceedings

gao-etal-2022-retrieval

Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.92/

Gao, Yifan and Yin, Qingyu and Li, Zheng and Meng, Rui and Zhao, Tong and Yin, Bing and King, Irwin and Lyu, Michael

Findings of the Association for Computational Linguistics: NAACL 2022

1233--1246

Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text. Despite its recent flourishing, keyphrase generation on non-English languages haven`t been vastly investigated. In this paper, we call attention to a new setting named multilingual keyphrase generation and we contribute two new datasets, EcommerceMKP and AcademicMKP, covering six languages. Technically, we propose a retrieval-augmented method for multilingual keyphrase generation to mitigate the data shortage problem in non-English languages. The retrieval-augmented model leverages keyphrase annotations in English datasets to facilitate generating keyphrases in low-resource languages. Given a non-English passage, a cross-lingual dense passage retrieval module finds relevant English passages. Then the associated English keyphrases serve as external knowledge for keyphrase generation in the current language. Moreover, we develop a retriever-generator iterative training algorithm to mine pseudo parallel passage pairs to strengthen the cross-lingual passage retriever. Comprehensive experiments and ablations show that the proposed approach outperforms all baselines.

null

10.18653/v1/2022.findings-naacl.92

null

26,349

inproceedings

sharma-buduru-2022-fatnet

{FA}t{N}et: Cost-Effective Approach Towards Mitigating the Linguistic Bias in Speaker Verification Systems

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.93/

Sharma, Divya and Buduru, Arun Balaji

Findings of the Association for Computational Linguistics: NAACL 2022

1247--1258

Linguistic bias in Deep Neural Network (DNN) based Natural Language Processing (NLP) systems is a critical problem that needs attention. The problem further intensifies in the case of security systems, such as speaker verification, where fairness is essential. Speaker verification systems are intelligent systems that determine if two speech recordings belong to the same speaker. Such human-oriented security systems should be usable by diverse people speaking varied languages. Thus, a speaker verification system trained on speech in one language should generalize when tested for other languages. However, DNN-based models are often language-dependent. Previous works explore domain adaptation to fine-tune the pre-trained model for out-of-domain languages. Fine-tuning the model individually for each existing language is expensive. Hence, it limits the usability of the system. This paper proposes the cost-effective idea of integrating a lightweight embedding with existing speaker verification systems to mitigate linguistic bias without adaptation. This work is motivated by the theoretical hypothesis that attentive-frames could help generate language-agnostic embeddings. For scientific validation of this hypothesis, we propose two frame-attentive networks and investigate the effect of their integration with baselines for twelve languages. Empirical results suggest that frame-attentive embedding can cost-effectively reduce linguistic bias and enhance the usability of baselines.

null

10.18653/v1/2022.findings-naacl.93

null

26,350

inproceedings

hardalov-etal-2022-survey

A Survey on Stance Detection for Mis- and Disinformation Identification

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.94/

Hardalov, Momchil and Arora, Arnav and Nakov, Preslav and Augenstein, Isabelle

Findings of the Association for Computational Linguistics: NAACL 2022

1259--1277

Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information). Stance detection has been framed in different ways, including (a) as a component of fact-checking, rumour detection, and detecting previously fact-checked claims, or (b) as a task in its own right. While there have been prior efforts to contrast stance detection with other related tasks such as argumentation mining and sentiment analysis, there is no existing survey on examining the relationship between stance detection and mis- and disinformation detection. Here, we aim to bridge this gap by reviewing and analysing existing work in this area, with mis- and disinformation in focus, and discussing lessons learnt and future challenges.

null

10.18653/v1/2022.findings-naacl.94

null

26,351

inproceedings

liu-etal-2022-syntax

Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.95/

Liu, Jin and Fan, Chongfeng and Fengyu, Zhou and Xu, Huijuan

Findings of the Association for Computational Linguistics: NAACL 2022

1278--1291

The knowledge graph (KG) stores a large amount of structural knowledge, while it is not easy for direct human understanding. Knowledge graph-to-text (KG-to-text) generation aims to generate easy-to-understand sentences from the KG, and at the same time, maintains semantic consistency between generated sentences and the KG. Existing KG-to-text generation methods phrase this task as a sequence-to-sequence generation task with linearized KG as input and consider the consistency issue of the generated texts and KG through a simple selection between decoded sentence word and KG node word at each time step. However, the linearized KG order is obtained through a heuristic search without data-driven optimization. In this paper, we optimize the knowledge description order prediction under the order supervision extracted from the caption and further enhance the consistency of the generated sentences and KG through syntactic and semantic regularization. We incorporate the Part-of-Speech (POS) syntactic tags to constrain the positions to copy words from the KG and employ a semantic context scoring function to evaluate the semantic fitness for each word in its local context when decoding each word in the generated sentence. Extensive experiments are conducted on two datasets, WebNLG and DART, and achieve state-of-the-art performances. Our code is now public available.

null

10.18653/v1/2022.findings-naacl.95

null

26,352

inproceedings

ji-etal-2022-answer

To Answer or Not To Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.96/

Ji, Yunjie and Chen, Liangyu and Dou, Chenxiao and Ma, Baochang and Li, Xiangang

Findings of the Association for Computational Linguistics: NAACL 2022

1292--1300

Machine Reading Comprehension with Unanswerable Questions is a difficult NLP task, challenged by the questions which can not be answered from passages. It is observed that subtle literal changes often make an answerable question unanswerable, however, most MRC models fail to recognize such changes. To address this problem, in this paper, we propose a span-based method of Contrastive Learning (spanCL) which explicitly contrast answerable questions with their answerable and unanswerable counterparts at the answer span level. With spanCL, MRC models are forced to perceive crucial semantic changes from slight literal differences. Experiments on SQuAD 2.0 dataset show that spanCL can improve baselines significantly, yielding 0.86 2.14 absolute EM improvements. Additional experiments also show that spanCL is an effective way to utilize generated questions.

null

10.18653/v1/2022.findings-naacl.96

null

26,353

inproceedings

gupta-etal-2022-target

Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.97/

Gupta, Prakhar and Jhamtani, Harsh and Bigham, Jeffrey

Findings of the Association for Computational Linguistics: NAACL 2022

1301--1317

Target-guided response generation enables dialogue systems to smoothly transition a conversation from a dialogue context toward a target sentence. Such control is useful for designing dialogue systems that direct a conversation toward specific goals, such as creating non-obtrusive recommendations or introducing new topics in the conversation. In this paper, we introduce a new technique for target-guided response generation, which first finds a bridging path of commonsense knowledge concepts between the source and the target, and then uses the identified bridging path to generate transition responses. Additionally, we propose techniques to re-purpose existing dialogue datasets for target-guided generation. Experiments reveal that the proposed techniques outperform various baselines on this task. Finally, we observe that the existing automated metrics for this task correlate poorly with human judgement ratings. We propose a novel evaluation metric that we demonstrate is more reliable for target-guided response evaluation. Our work generally enables dialogue system designers to exercise more control over the conversations that their systems produce.

null

10.18653/v1/2022.findings-naacl.97

null

26,354

inproceedings

bhattacharjee-etal-2022-banglabert

{B}angla{BERT}: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in {B}angla

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.98/

Bhattacharjee, Abhik and Hasan, Tahmid and Ahmad, Wasi and Mubasshir, Kazi Samin and Islam, Md Saiful and Iqbal, Anindya and Rahman, M. Sohel and Shahriyar, Rifat

Findings of the Association for Computational Linguistics: NAACL 2022

1318--1327

In this work, we introduce BanglaBERT, a BERT-based Natural Language Understanding (NLU) model pretrained in Bangla, a widely spoken yet low-resource language in the NLP literature. To pretrain BanglaBERT, we collect 27.5 GB of Bangla pretraining data (dubbed {\textquoteleft}Bangla2B+') by crawling 110 popular Bangla sites. We introduce two downstream task datasets on natural language inference and question answering and benchmark on four diverse NLU tasks covering text classification, sequence labeling, and span prediction. In the process, we bring them under the first-ever Bangla Language Understanding Benchmark (BLUB). BanglaBERT achieves state-of-the-art results outperforming multilingual and monolingual models. We are making the models, datasets, and a leaderboard publicly available at \url{https://github.com/csebuetnlp/banglabert} to advance Bangla NLP.

null

10.18653/v1/2022.findings-naacl.98

null

26,355

inproceedings

zhang-etal-2022-allsh

{ALLSH}: Active Learning Guided by Local Sensitivity and Hardness

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.99/

Zhang, Shujian and Gong, Chengyue and Liu, Xingchao and He, Pengcheng and Chen, Weizhu and Zhou, Mingyuan

Findings of the Association for Computational Linguistics: NAACL 2022

1328--1342

Active learning, which effectively collects informative unlabeled data for annotation, reduces the demand for labeled data. In this work, we propose to retrieve unlabeled samples with a local sensitivity and hardness-aware acquisition function. The proposed method generates data copies through local perturbations and selects data points whose predictive likelihoods diverge the most from their copies. We further empower our acquisition function by injecting the select-worst case perturbation. Our method achieves consistent gains over the commonly used active learning strategies in various classification tasks. Furthermore, we observe consistent improvements over the baselines on the study of prompt selection in prompt-based few-shot learning. These experiments demonstrate that our acquisition guided by local sensitivity and hardness can be effective and beneficial for many NLP tasks.

null

10.18653/v1/2022.findings-naacl.99

null

26,356

inproceedings

shao-etal-2022-low

Low-resource Entity Set Expansion: A Comprehensive Study on User-generated Text

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.100/

Shao, Yutong and Bhutani, Nikita and Rahman, Sajjadur and Hruschka, Estevam

Findings of the Association for Computational Linguistics: NAACL 2022

1343--1353

Entity set expansion (ESE) aims at obtaining a more complete set of entities given a textual corpus and a seed set of entities of a concept. Although it is a critical task in many NLP applications, existing benchmarks are limited to well-formed text (e.g., Wikipedia) and well-defined concepts (e.g., countries and diseases). Furthermore, only a small number of predictions are evaluated compared to the actual size of an entity set. A rigorous assessment of ESE methods warrants more comprehensive benchmarks and evaluation. In this paper, we consider user-generated text to understand the generalizability of ESE methods. We develop new benchmarks and propose more rigorous evaluation metrics for assessing the performance of ESE methods. Additionally, we identify phenomena such as non-named entities, multifaceted entities, vague concepts that are more prevalent in user-generated text than well-formed text, and use them to profile ESE methods. We observe that the strong performance of state-of-the-art ESE methods does not generalize well to user-generated text. We conduct comprehensive empirical analysis and draw insights from the findings.

null

10.18653/v1/2022.findings-naacl.100

null

26,357

inproceedings

liu-etal-2022-politics

{POLITICS}: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.101/

Liu, Yujian and Zhang, Xinliang Frederick and Wegsman, David and Beauchamp, Nicholas and Wang, Lu

Findings of the Association for Computational Linguistics: NAACL 2022

1354--1374

Ideology is at the core of political science research. Yet, there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. To this end, we study Pretrained Language Models using novel ideology-driven pretraining objectives that rely on the comparison of articles on the same story written by media of different ideologies. We further collect a large-scale dataset, consisting of more than 3.6M political news articles, for pretraining. Our model POLITICS outperforms strong baselines and the previous state-of-the-art models on ideology prediction and stance detection tasks. Further analyses show that POLITICS is especially good at understanding long or formally written texts, and is also robust in few-shot learning scenarios.

null

10.18653/v1/2022.findings-naacl.101

null

26,358

inproceedings

chen-etal-2022-empowering

Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.102/

Chen, Yifan and Hazarika, Devamanyu and Namazifar, Mahdi and Liu, Yang and Jin, Di and Hakkani-Tur, Dilek

Findings of the Association for Computational Linguistics: NAACL 2022

1375--1388

The massive amount of trainable parameters in the pre-trained language models (PLMs) makes them hard to be deployed to multiple downstream tasks. To address this issue, parameter-efficient transfer learning methods have been proposed to tune only a few parameters during fine-tuning while freezing the rest. This paper looks at existing methods along this line through the \textit{kernel lens}. Motivated by the connection between self-attention in transformer-based PLMs and kernel learning, we propose \textit{kernel-wise adapters}, namely \textit{Kernel-mix}, that utilize the kernel structure in self-attention to guide the assignment of the tunable parameters. These adapters use guidelines found in classical kernel learning and enable separate parameter tuning for each attention head. Our empirical results, over a diverse set of natural language generation and understanding tasks, show that our proposed adapters can attain or improve the strong performance of existing baselines.

null

10.18653/v1/2022.findings-naacl.102

null

26,359

inproceedings

haidar-etal-2022-rail

{RAIL}-{KD}: {RA}ndom Intermediate Layer Mapping for Knowledge Distillation

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.103/

Haidar, Md Akmal and Anchuri, Nithin and Rezagholizadeh, Mehdi and Ghaddar, Abbas and Langlais, Philippe and Poupart, Pascal

Findings of the Association for Computational Linguistics: NAACL 2022

1389--1400

Intermediate layer knowledge distillation (KD) can improve the standard KD technique (which only targets the output of teacher and student models) especially over large pre-trained language models. However, intermediate layer distillation suffers from excessive computational burdens and engineering efforts required for setting up a proper layer mapping. To address these problems, we propose a RAndom Intermediate Layer Knowledge Distillation (RAIL-KD) approach in which, intermediate layers from the teacher model are selected randomly to be distilled into the intermediate layers of the student model. This randomized selection enforces that all teacher layers are taken into account in the training process, while reducing the computational cost of intermediate layer distillation. Also, we show that it acts as a regularizer for improving the generalizability of the student model. We perform extensive experiments on GLUE tasks as well as on out-of-domain test sets. We show that our proposed RAIL-KD approach outperforms other state-of-the-art intermediate layer KD methods considerably in both performance and training-time.

null

10.18653/v1/2022.findings-naacl.103

null

26,360

inproceedings

yang-etal-2022-unbiased

Unbiased Math Word Problems Benchmark for Mitigating Solving Bias

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.104/

Yang, Zhicheng and Qin, Jinghui and Chen, Jiaqi and Liang, Xiaodan

Findings of the Association for Computational Linguistics: NAACL 2022

1401--1408

In this paper, we revisit the solving bias when evaluating models on current Math Word Problem (MWP) benchmarks. However, current solvers exist solving bias which consists of data bias and learning bias due to biased dataset and improper training strategy. Our experiments verify MWP solvers are easy to be biased by the biased training datasets which do not cover diverse questions for each problem narrative of all MWPs, thus a solver can only learn shallow heuristics rather than deep semantics for understanding problems. Besides, an MWP can be naturally solved by multiple equivalent equations while current datasets take only one of the equivalent equations as ground truth, forcing the model to match the labeled ground truth and ignoring other equivalent equations. Here, we first introduce a novel MWP dataset named UnbiasedMWP which is constructed by varying the grounded expressions in our collected data and annotating them with corresponding multiple new questions manually. Then, to further mitigate learning bias, we propose a Dynamic Target Selection (DTS) Strategy to dynamically select more suitable target expressions according to the longest prefix match between the current model output and candidate equivalent equations which are obtained by applying commutative law during training. The results show that our UnbiasedMWP has significantly fewer biases than its original data and other datasets, posing a promising benchmark for fairly evaluating the solvers' reasoning skills rather than matching nearest neighbors. And the solvers trained with our DTS achieve higher accuracies on multiple MWP benchmarks. The source code is available at \url{https://github.com/yangzhch6/UnbiasedMWP}.

null

10.18653/v1/2022.findings-naacl.104

null

26,361

inproceedings

feng-etal-2022-learn

Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translation

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.105/

Feng, Yukun and Li, Feng and Song, Ziang and Zheng, Boyuan and Koehn, Philipp

Findings of the Association for Computational Linguistics: NAACL 2022

1409--1420

The Transformer architecture has led to significant gains in machine translation. However, most studies focus on only sentence-level translation without considering the context dependency within documents, leading to the inadequacy of document-level coherence. Some recent research tried to mitigate this issue by introducing an additional context encoder or translating with multiple sentences or even the entire document. Such methods may lose the information on the target side or have an increasing computational complexity as documents get longer. To address such problems, we introduce a recurrent memory unit to the vanilla Transformer, which supports the information exchange between the sentence and previous context. The memory unit is recurrently updated by acquiring information from sentences, and passing the aggregated knowledge back to subsequent sentence states. We follow a two-stage training strategy, in which the model is first trained at the sentence level and then finetuned for document-level translation. We conduct experiments on three popular datasets for document-level machine translation and our model has an average improvement of 0.91 s-BLEU over the sentence-level baseline. We also achieve state-of-the-art results on TED and News, outperforming the previous work by 0.36 s-BLEU and 1.49 d-BLEU on average.

null

10.18653/v1/2022.findings-naacl.105

null

26,362

inproceedings

nishida-etal-2022-improving

Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.106/

Nishida, Kosuke and Nishida, Kyosuke and Nishioka, Shuichi

Findings of the Association for Computational Linguistics: NAACL 2022

1421--1430

Humans can obtain the knowledge of novel visual concepts from language descriptions, and we thus use the few-shot image classification task to investigate whether a machine learning model can have this capability. Our proposed model, LIDE (Learning from Image and DEscription), has a text decoder to generate the descriptions and a text encoder to obtain the text representations of machine- or user-generated descriptions. We confirmed that LIDE with machine-generated descriptions outperformed baseline models. Moreover, the performance was improved further with high-quality user-generated descriptions. The generated descriptions can be viewed as the explanations of the model`s predictions, and we observed that such explanations were consistent with prediction results. We also investigated why the language description improves the few-shot image classification performance by comparing the image representations and the text representations in the feature spaces.

null

10.18653/v1/2022.findings-naacl.106

null

26,363

inproceedings

qi-etal-2022-information

All Information is Valuable: Question Matching over Full Information Transmission Network

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.107/

Qi, Le and Zhang, Yu and Yin, Qingyu and Zheng, Guidong and Junjie, Wen and Li, Jinlong and Liu, Ting

Findings of the Association for Computational Linguistics: NAACL 2022

1431--1440

Question matching is the task of identifying whether two questions have the same intent. For better reasoning the relationship between questions, existing studies adopt multiple interaction modules and perform multi-round reasoning via deep neural networks. In this process, there are two kinds of critical information that are commonly employed: the representation information of original questions and the interactive information between pairs of questions. However, previous studies tend to transmit only one kind of information, while failing to utilize both kinds of information simultaneously. To address this problem, in this paper, we propose a Full Information Transmission Network (FITN) that can transmit both representation and interactive information together in a simultaneous fashion. More specifically, we employ a novel memory-based attention for keeping and transmitting the interactive information through a global interaction matrix. Besides, we apply an original-average mixed connection method to effectively transmit the representation information between different reasoning rounds, which helps to preserve the original representation features of questions along with the historical hidden features. Experiments on two standard benchmarks demonstrate that our approach outperforms strong baseline models.

null

10.18653/v1/2022.findings-naacl.107

null

26,364

inproceedings

yang-etal-2022-pathway2text

{P}athway2{T}ext: Dataset and Method for Biomedical Pathway Description Generation

Carpuat, Marine and de Marneffe, Marie-Catherine and Meza Ruiz, Ivan Vladimir

jul

2022

Seattle, United States

Association for Computational Linguistics

https://aclanthology.org/2022.findings-naacl.108/

Yang, Junwei and Liu, Zequn and Zhang, Ming and Wang, Sheng

Findings of the Association for Computational Linguistics: NAACL 2022

1441--1454

Biomedical pathways have been extensively used to characterize the mechanism of complex diseases. One essential step in biomedical pathway analysis is to curate the description of a pathway based on its graph structure and node features. Neural text generation could be a plausible technique to circumvent the tedious manual curation. In this paper, we propose a new dataset Pathway2Text, which contains 2,367 pairs of biomedical pathways and textual descriptions. All pathway graphs are experimentally derived or manually curated. All textual descriptions are written by domain experts. We form this problem as a Graph2Text task and propose a novel graph-based text generation approach $k$NN-Graph2Text, which explicitly exploited descriptions of similar graphs to generate new descriptions. We observed substantial improvement of our method on both Graph2Text and the reverse task of Text2Graph. We further illustrated how our dataset can be used as a novel benchmark for biomedical named entity recognition. Collectively, we envision our method will become an important benchmark for evaluating Graph2Text methods and advance biomedical research for complex diseases.

null

10.18653/v1/2022.findings-naacl.108

null

26,365