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

inproceedings

wagner-etal-2022-topical

Topical Segmentation of Spoken Narratives: A Test Case on Holocaust Survivor Testimonies

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.457/

Wagner, Eitan and Keydar, Renana and Pinchevski, Amit and Abend, Omri

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6809--6821

The task of topical segmentation is well studied, but previous work has mostly addressed it in the context of structured, well-defined segments, such as segmentation into paragraphs, chapters, or segmenting text that originated from multiple sources. We tackle the task of segmenting running (spoken) narratives, which poses hitherto unaddressed challenges. As a test case, we address Holocaust survivor testimonies, given in English. Other than the importance of studying these testimonies for Holocaust research, we argue that they provide an interesting test case for topical segmentation, due to their unstructured surface level, relative abundance (tens of thousands of such testimonies were collected), and the relatively confined domain that they cover. We hypothesize that boundary points between segments correspond to low mutual information between the sentences proceeding and following the boundary. Based on this hypothesis, we explore a range of algorithmic approaches to the task, building on previous work on segmentation that uses generative Bayesian modeling and state-of-the-art neural machinery. Compared to manually annotated references, we find that the developed approaches show considerable improvements over previous work.

null

10.18653/v1/2022.emnlp-main.457

null

27,587

inproceedings

huang-etal-2022-unifying

Unifying the Convergences in Multilingual Neural Machine Translation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.458/

Huang, Yichong and Feng, Xiaocheng and Geng, Xinwei and Qin, Bing

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6822--6835

Although all-in-one-model multilingual neural machine translation (MNMT) has achieved remarkable progress, the convergence inconsistency in the joint training is ignored, i.e., different language pairs reaching convergence in different epochs. This leads to the trained MNMT model over-fitting low-resource language translations while under-fitting high-resource ones. In this paper, we propose a novel training strategy named LSSD (LanguageSpecific Self-Distillation), which can alleviate the convergence inconsistency and help MNMT models achieve the best performance on each language pair simultaneously. Specifically, LSSD picks up language-specific best checkpoints for each language pair to teach the current model on the fly. Furthermore, we systematically explore three sample-level manipulations of knowledge transferring. Experimental results on three datasets show that LSSD obtains consistent improvements towards all language pairs and achieves the state-of-the-art.

null

10.18653/v1/2022.emnlp-main.458

null

27,588

inproceedings

jiang-etal-2022-modeling

Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.459/

Jiang, Chengyue and Jiang, Yong and Wu, Weiqi and Xie, Pengjun and Tu, Kewei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6836--6847

Ultra-fine entity typing (UFET) aims to predict a wide range of type phrases that correctly describe the categories of a given entity mention in a sentence. Most recent works infer each entity type independently, ignoring the correlations between types, e.g., when an entity is inferred as a \textit{president}, it should also be a \textit{politician} and a \textit{leader}. To this end, we use an undirected graphical model called pairwise conditional random field (PCRF) to formulate the UFET problem, in which the type variables are not only unarily influenced by the input but also pairwisely relate to all the other type variables. We use various modern backbones for entity typing to compute unary potentials, and derive pairwise potentials from type phrase representations that both capture prior semantic information and facilitate accelerated inference. We use mean-field variational inference for efficient type inference on very large type sets and unfold it as a neural network module to enable end-to-end training. Experiments on UFET show that the Neural-PCRF consistently outperforms its backbones with little cost and results in a competitive performance against cross-encoder based SOTA while being \textit{thousands of times} faster. We also find Neural-PCRF effective on a widely used fine-grained entity typing dataset with a smaller type set. We pack Neural-PCRF as a network module that can be plugged onto multi-label type classifiers with ease and release it in .

null

10.18653/v1/2022.emnlp-main.459

null

27,589

inproceedings

chakrabarty-etal-2022-help

\textit{Help me write a poem}: Instruction Tuning as a Vehicle for Collaborative Poetry Writing

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.460/

Chakrabarty, Tuhin and Padmakumar, Vishakh and He, He

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6848--6863

Recent work in training large language models (LLMs) to follow natural language instructions has opened up exciting opportunities for natural language interface design. Building on the prior success of large language models in the realm of computer assisted creativity, in this work, we present \textit{CoPoet}, a collaborative poetry writing system, with the goal of to study if LLM`s actually improve the quality of the generated content. In contrast to auto-completing a user`s text, CoPoet is controlled by user instructions that specify the attributes of the desired text, such as \textit{Write a sentence about {\textquoteleft}love'} or \textit{Write a sentence ending in {\textquoteleft}fly'}. The core component of our system is a language model fine-tuned on a diverse collection of instructions for poetry writing. Our model is not only competitive to publicly available LLMs trained on instructions (InstructGPT), but also capable of satisfying unseen compositional instructions. A study with 15 qualified crowdworkers shows that users successfully write poems with CoPoet on diverse topics ranging from \textit{Monarchy} to \textit{Climate change}, which are preferred by third-party evaluators over poems written without the system.

null

10.18653/v1/2022.emnlp-main.460

null

27,590

inproceedings

li-etal-2022-open

Open Relation and Event Type Discovery with Type Abstraction

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.461/

Li, Sha and Ji, Heng and Han, Jiawei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6864--6877

Conventional {\textquotedblleft}closed-world{\textquotedblright} information extraction (IE) approaches rely on human ontologies to define the scope for extraction. As a result, such approaches fall short when applied to new domains. This calls for systems that can automatically infer new types from given corpora, a task which we refer to as type discovery.To tackle this problem, we introduce the idea of type abstraction, where the model is prompted to generalize and name the type. Then we use the similarity between inferred names to induce clusters. Observing that this abstraction-based representation is often complementary to the entity/trigger token representation, we set up these two representations as two views and design our model as a co-training framework. Our experiments on multiple relation extraction and event extraction datasets consistently show the advantage of our type abstraction approach.

null

10.18653/v1/2022.emnlp-main.461

null

27,591

inproceedings

liu-etal-2022-enhancing-multilingual

Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.462/

Liu, Linlin and Li, Xin and He, Ruidan and Bing, Lidong and Joty, Shafiq and Si, Luo

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6878--6890

Knowledge-enhanced language representation learning has shown promising results across various knowledge-intensive NLP tasks. However, prior methods are limited in efficient utilization of multilingual knowledge graph (KG) data for language model (LM) pretraining. They often train LMs with KGs in indirect ways, relying on extra entity/relation embeddings to facilitate knowledge injection. In this work, we explore methods to make better use of the multilingual annotation and language agnostic property of KG triples, and present novel knowledge based multilingual language models (KMLMs) trained directly on the knowledge triples. We first generate a large amount of multilingual synthetic sentences using the Wikidata KG triples. Then based on the intra- and inter-sentence structures of the generated data, we design pretraining tasks to enable the LMs to not only memorize the factual knowledge but also learn useful logical patterns. Our pretrained KMLMs demonstrate significant performance improvements on a wide range of knowledge-intensive cross-lingual tasks, including named entity recognition (NER), factual knowledge retrieval, relation classification, and a newly designed logical reasoning task.

null

10.18653/v1/2022.emnlp-main.462

null

27,592

inproceedings

gong-etal-2022-revisiting

Revisiting Grammatical Error Correction Evaluation and Beyond

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.463/

Gong, Peiyuan and Liu, Xuebo and Huang, Heyan and Zhang, Min

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6891--6902

Pretraining-based (PT-based) automatic evaluation metrics (e.g., BERTScore and BARTScore) have been widely used in several sentence generation tasks (e.g., machine translation and text summarization) due to their better correlation with human judgments over traditional overlap-based methods. Although PT-based methods have become the de facto standard for training grammatical error correction (GEC) systems, GEC evaluation still does not benefit from pretrained knowledge. This paper takes the first step towards understanding and improving GEC evaluation with pretraining. We first find that arbitrarily applying PT-based metrics to GEC evaluation brings unsatisfactory correlation results because of the excessive attention to inessential systems outputs (e.g., unchanged parts). To alleviate the limitation, we propose a novel GEC evaluation metric to achieve the best of both worlds, namely PT-M2 which only uses PT-based metrics to score those corrected parts. Experimental results on the CoNLL14 evaluation task show that PT-M2 significantly outperforms existing methods, achieving a new state-of-the-art result of 0.949 Pearson correlation. Further analysis reveals that PT-M2 is robust to evaluate competitive GEC systems. Source code and scripts are freely available at https://github.com/pygongnlp/PT-M2.

null

10.18653/v1/2022.emnlp-main.463

null

27,593

inproceedings

nan-etal-2022-r2d2

{R}2{D}2: Robust Data-to-Text with Replacement Detection

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.464/

Nan, Linyong and Flores, Lorenzo Jaime and Zhao, Yilun and Liu, Yixin and Benson, Luke and Zou, Weijin and Radev, Dragomir

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6903--6917

Unfaithful text generation is a common problem for text generation systems. In the case of Data-to-Text (D2T) systems, the factuality of the generated text is particularly crucial for any real-world applications. We introduce R2D2, a training framework that addresses unfaithful Data-to-Text generation by training a system both as a generator and a faithfulness discriminator with additional replacement detection and unlikelihood learning tasks. To facilitate such training, we propose two methods for sampling unfaithful sentences. We argue that the poor entity retrieval capability of D2T systems is one of the primary sources of unfaithfulness, so in addition to the existing metrics, we further propose named entity based metrics to evaluate the fidelity of D2T generations. Our experimental results show that R2D2 systems could effectively mitigate the unfaithful text generation, and they achieve new state-of-theart results on FeTaQA, LogicNLG, and ToTTo, all with significant improvements.

null

10.18653/v1/2022.emnlp-main.464

null

27,594

inproceedings

putri-oh-2022-idk

{IDK}-{MRC}: Unanswerable Questions for {I}ndonesian Machine Reading Comprehension

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.465/

Putri, Rifki Afina and Oh, Alice

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6918--6933

Machine Reading Comprehension (MRC) has become one of the essential tasks in Natural Language Understanding (NLU) as it is often included in several NLU benchmarks (Liang et al., 2020; Wilie et al., 2020). However, most MRC datasets only have answerable question type, overlooking the importance of unanswerable questions. MRC models trained only on answerable questions will select the span that is most likely to be the answer, even when the answer does not actually exist in the given passage (Rajpurkar et al., 2018). This problem especially remains in medium- to low-resource languages like Indonesian. Existing Indonesian MRC datasets (Purwarianti et al., 2007; Clark et al., 2020) are still inadequate because of the small size and limited question types, i.e., they only cover answerable questions. To fill this gap, we build a new Indonesian MRC dataset called I(n)don`tKnow- MRC (IDK-MRC) by combining the automatic and manual unanswerable question generation to minimize the cost of manual dataset construction while maintaining the dataset quality. Combined with the existing answerable questions, IDK-MRC consists of more than 10K questions in total. Our analysis shows that our dataset significantly improves the performance of Indonesian MRC models, showing a large improvement for unanswerable questions.

null

10.18653/v1/2022.emnlp-main.465

null

27,595

inproceedings

wang-etal-2022-xlm

{XLM}-{D}: Decorate Cross-lingual Pre-training Model as Non-Autoregressive Neural Machine Translation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.466/

Wang, Yong and He, Shilin and Chen, Guanhua and Chen, Yun and Jiang, Daxin

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6934--6946

Pre-training language models have achieved thriving success in numerous natural language understanding and autoregressive generation tasks, but non-autoregressive generation in applications such as machine translation has not sufficiently benefited from the pre-training paradigm. In this work, we establish the connection between a pre-trained masked language model (MLM) and non-autoregressive generation on machine translation. From this perspective, we present XLM-D, which seamlessly transforms an off-the-shelf cross-lingual pre-training model into a non-autoregressive translation (NAT) model with a lightweight yet effective decorator. Specifically, the decorator ensures the representation consistency of the pre-trained model and brings only one additional trainable parameter. Extensive experiments on typical translation datasets show that our models obtain state-of-the-art performance while realizing the inference speed-up by 19.9x. One striking result is that on WMT14 En-De, our XLM-D obtains 29.80 BLEU points with multiple iterations, which outperforms the previous mask-predict model by 2.77 points.

null

10.18653/v1/2022.emnlp-main.466

null

27,596

inproceedings

dai-etal-2022-cross

Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.467/

Dai, Qin and Heinzerling, Benjamin and Inui, Kentaro

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6947--6958

Bi-encoder architectures for distantly-supervised relation extraction are designed to make use of the complementary information found in text and knowledge graphs (KG).However, current architectures suffer from two drawbacks. They either do not allow any sharing between the text encoder and the KG encoder at all, or, in case of models with KG-to-text attention, only share information in one direction. Here, we introduce cross-stitch bi-encoders, which allow full interaction between the text encoder and the KG encoder via a cross-stitch mechanism. The cross-stitch mechanism allows sharing and updating representations between the two encoders at any layer, with the amount of sharing being dynamically controlled via cross-attention-based gates. Experimental results on two relation extraction benchmarks from two different domains show that enabling full interaction between the two encoders yields strong improvements.

null

10.18653/v1/2022.emnlp-main.467

null

27,597

inproceedings

liu-etal-2022-assist

Assist Non-native Viewers: Multimodal Cross-Lingual Summarization for How2 Videos

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.468/

Liu, Nayu and Wei, Kaiwen and Sun, Xian and Yu, Hongfeng and Yao, Fanglong and Jin, Li and Zhi, Guo and Xu, Guangluan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6959--6969

Multimodal summarization for videos aims to generate summaries from multi-source information (videos, audio transcripts), which has achieved promising progress. However, existing works are restricted to monolingual video scenarios, ignoring the demands of non-native video viewers to understand the cross-language videos in practical applications. It stimulates us to propose a new task, named Multimodal Cross-Lingual Summarization for videos (MCLS), which aims to generate cross-lingual summaries from multimodal inputs of videos. First, to make it applicable to MCLS scenarios, we conduct a Video-guided Dual Fusion network (VDF) that integrates multimodal and cross-lingual information via diverse fusion strategies at both encoder and decoder. Moreover, to alleviate the problem of high annotation costs and limited resources in MCLS, we propose a triple-stage training framework to assist MCLS by transferring the knowledge from monolingual multimodal summarization data, which includes: 1) multimodal summarization on sufficient prevalent language videos with a VDF model; 2) knowledge distillation (KD) guided adjustment on bilingual transcripts; 3) multimodal summarization for cross-lingual videos with a KD induced VDF model. Experiment results on the reorganized How2 dataset show that the VDF model alone outperforms previous methods for multimodal summarization, and the performance further improves by a large margin via the proposed triple-stage training framework.

null

10.18653/v1/2022.emnlp-main.468

null

27,598

inproceedings

deng-etal-2022-pacific

{PACIFIC}: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.469/

Deng, Yang and Lei, Wenqiang and Zhang, Wenxuan and Lam, Wai and Chua, Tat-Seng

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6970--6984

To facilitate conversational question answering (CQA) over hybrid contexts in finance, we present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numerical reasoning, and (iii) hybrid context of tables and text. A new task is defined accordingly to study Proactive Conversational Question Answering (PCQA), which combines clarification question generation and CQA. In addition, we propose a novel method, namely UniPCQA, to adapt a hybrid format of input and output content in PCQA into the Seq2Seq problem, including the reformulation of the numerical reasoning process as code generation. UniPCQA performs multi-task learning over all sub-tasks in PCQA and incorporates a simple ensemble strategy to alleviate the error propagation issue in the multi-task learning by cross-validating top-$k$ sampled Seq2Seq outputs. We benchmark the PACIFIC dataset with extensive baselines and provide comprehensive evaluations on each sub-task of PCQA.

null

10.18653/v1/2022.emnlp-main.469

null

27,599

inproceedings

li-yuan-2022-generative

Generative Data Augmentation with Contrastive Learning for Zero-Shot Stance Detection

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.470/

Li, Yang and Yuan, Jiawei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6985--6995

Stance detection aims to identify whether the author of an opinionated text is in favor of, against, or neutral towards a given target. Remarkable success has been achieved when sufficient labeled training data is available. However, it is labor-intensive to annotate sufficient data and train the model for every new target.Therefore, zero-shot stance detection, aiming at identifying stances of unseen targets with seen targets, has gradually attracted attention. Among them, one of the important challenges is to reduce the domain transfer between seen and unseen targets. To tackle this problem, we propose a generative data augmentation approach to generate training samples containing targets and stances for testing data, and map the real samples and generated synthetic samples into the same embedding space with contrastive learning, then perform the final classification based on the augmented data. We evaluate our proposed model on two benchmark datasets. Experimental results show that our approach achieves state-of-the-art performance on most topics in the task of zero-shot stance detection.

null

10.18653/v1/2022.emnlp-main.470

null

27,600

inproceedings

zhang-lu-2022-better

Better Few-Shot Relation Extraction with Label Prompt Dropout

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.471/

Zhang, Peiyuan and Lu, Wei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

6996--7006

Few-shot relation extraction aims to learn to identify the relation between two entities based on very limited training examples. Recent efforts found that textual labels (i.e., relation names and relation descriptions) could be extremely useful for learning class representations, which will benefit the few-shot learning task. However, what is the best way to leverage such label information in the learning process is an important research question. Existing works largely assume such textual labels are always present during both learning and prediction. In this work, we argue that such approaches may not always lead to optimal results. Instead, we present a novel approach called label prompt dropout, which randomly removes label descriptions in the learning process. Our experiments show that our approach is able to lead to improved class representations, yielding significantly better results on the few-shot relation extraction task.

null

10.18653/v1/2022.emnlp-main.471

null

27,601

inproceedings

kim-etal-2022-break

Break it Down into {BTS}: Basic, Tiniest Subword Units for {K}orean

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.472/

Kim, Nayeon and Park, Jun-Hyung and Choi, Joon-Young and Jeon, Eojin and Kang, Youjin and Lee, SangKeun

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7007--7024

We introduce Basic, Tiniest Subword (BTS) units for the Korean language, which are inspired by the invention principle of Hangeul, the Korean writing system. Instead of relying on 51 Korean consonant and vowel letters, we form the letters from BTS units by adding strokes or combining them. To examine the impact of BTS units on Korean language processing, we develop a novel BTS-based word embedding framework that is readily applicable to various models. Our experiments reveal that BTS units significantly improve the performance of Korean word embedding on all intrinsic and extrinsic tasks in our evaluation. In particular, BTS-based word embedding outperforms the state-of-theart Korean word embedding by 11.8{\%} in word analogy. We further investigate the unique advantages provided by BTS units through indepth analysis.

null

10.18653/v1/2022.emnlp-main.472

null

27,602

inproceedings

qin-etal-2022-devil

The Devil in Linear Transformer

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.473/

Qin, Zhen and Han, Xiaodong and Sun, Weixuan and Li, Dongxu and Kong, Lingpeng and Barnes, Nick and Zhong, Yiran

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7025--7041

Linear transformers aim to reduce the quadratic space-time complexity of vanilla transformers. However, they usually suffer from degraded performances on various tasks and corpus. In this paper, we examine existing kernel-based linear transformers and identify two key issues that lead to such performance gaps: 1) unbounded gradients in the attention computation adversely impact the convergence of linear transformer models; 2) attention dilution which trivially distributes attention scores over long sequences while neglecting neighbouring structures. To address these issues, we first identify that the scaling of attention matrices is the devil in unbounded gradients, which turns out unnecessary in linear attention as we show theoretically and empirically. To this end, we propose a new linear attention that replaces the scaling operation with a normalization to stabilize gradients. For the issue of attention dilution, we leverage a diagonal attention to confine attention to only neighbouring tokens in early layers. Benefiting from the stable gradients and improved attention, our new linear transformer model, transNormer, demonstrates superior performance on text classification and language modeling tasks, as well as on the challenging Long-Range Arena benchmark, surpassing vanilla transformer and existing linear variants by a clear margin while being significantly more space-time efficient. The code is available at https://github.com/OpenNLPLab/Transnormer .

null

10.18653/v1/2022.emnlp-main.473

null

27,603

inproceedings

yang-etal-2022-zero

Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.474/

Yang, Ping and Wang, Junjie and Gan, Ruyi and Zhu, Xinyu and Zhang, Lin and Wu, Ziwei and Gao, Xinyu and Zhang, Jiaxing and Sakai, Tetsuya

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7042--7055

We propose a new paradigm for zero-shot learners that is format agnostic, i.e., it is compatible with any format and applicable to a list of language tasks, such as text classification, commonsense reasoning, coreference resolution, and sentiment analysis. Zero-shot learning aims to train a model on a given task such that it can address new learning tasks without any additional training. Our approach converts zero-shot learning into multiple-choice tasks, avoiding problems in commonly used large-scale generative models such as FLAN. It not only adds generalization ability to models but also significantly reduces the number of parameters. Our method shares the merits of efficient training and deployment. Our approach shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as natural language inference and text classification. Our model achieves this success with only 235M parameters, which is substantially smaller than state-of-the-art models with billions of parameters. The code and pre-trained models are available at https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/unimc .

null

10.18653/v1/2022.emnlp-main.474

null

27,604

inproceedings

li-etal-2022-hypoformer

Hypoformer: Hybrid Decomposition Transformer for Edge-friendly Neural Machine Translation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.475/

Li, Sunzhu and Zhang, Peng and Gan, Guobing and Lv, Xiuqing and Wang, Benyou and Wei, Junqiu and Jiang, Xin

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7056--7068

Transformer has been demonstrated effective in Neural Machine Translation (NMT). However, it is memory-consuming and time-consuming in edge devices, resulting in some difficulties for real-time feedback. To compress and accelerate Transformer, we propose a Hybrid Tensor-Train (HTT) decomposition, which retains full rank and meanwhile reduces operations and parameters. A Transformer using HTT, named Hypoformer, consistently and notably outperforms the recent light-weight SOTA methods on three standard translation tasks under different parameter and speed scales. In extreme low resource scenarios, Hypoformer has 7.1 points absolute improvement in BLEU and 1.27 X speedup than vanilla Transformer on IWSLT`14 De-En task.

null

10.18653/v1/2022.emnlp-main.475

null

27,605

inproceedings

liu-etal-2022-figmemes

{F}ig{M}emes: A Dataset for Figurative Language Identification in Politically-Opinionated Memes

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.476/

Liu, Chen and Geigle, Gregor and Krebs, Robin and Gurevych, Iryna

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7069--7086

Real-world politically-opinionated memes often rely on figurative language to cloak propaganda and radical ideas to help them spread. It is not only a scientific challenge to develop machine learning models to recognize them in memes, but also sociologically beneficial to understand hidden meanings at scale and raise awareness. These memes are fast-evolving (in both topics and visuals) and it remains unclear whether current multimodal machine learning models are robust to such distribution shifts. To enable future research into this area, we first present FigMemes, a dataset for figurative language classification in politically-opinionated memes. We evaluate the performance of state-of-the-art unimodal and multimodal models and provide comprehensive benchmark results. The key contributions of this proposed dataset include annotations of six commonly used types of figurative language in politically-opinionated memes, and a wide range of topics and visual styles.We also provide analyses on the ability of multimodal models to generalize across distribution shifts in memes. Our dataset poses unique machine learning challenges and our results show that current models have significant room for improvement in both performance and robustness to distribution shifts.

null

10.18653/v1/2022.emnlp-main.476

null

27,606

inproceedings

tang-etal-2022-unirel

{U}ni{R}el: Unified Representation and Interaction for Joint Relational Triple Extraction

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.477/

Tang, Wei and Xu, Benfeng and Zhao, Yuyue and Mao, Zhendong and Liu, Yifeng and Liao, Yong and Xie, Haiyong

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7087--7099

Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations. Existing works suffer from 1) heterogeneous representations of entities and relations, and 2) heterogeneous modeling of entity-entity interactions and entity-relation interactions. Therefore, the rich correlations are not fully exploited by existing works. In this paper, we propose UniRel to address these challenges. Specifically, we unify the representations of entities and relations by jointly encoding them within a concatenated natural language sequence, and unify the modeling of interactions with a proposed Interaction Map, which is built upon the off-the-shelf self-attention mechanism within any Transformer block. With comprehensive experiments on two popular relational triple extraction datasets, we demonstrate that UniRel is more effective and computationally efficient. The source code is available at https://github.com/wtangdev/UniRel.

null

10.18653/v1/2022.emnlp-main.477

null

27,607

inproceedings

chaudhury-etal-2022-x

{X}-{FACTOR}: A Cross-metric Evaluation of Factual Correctness in Abstractive Summarization

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.478/

Chaudhury, Subhajit and Swaminathan, Sarathkrishna and Gunasekara, Chulaka and Crouse, Maxwell and Ravishankar, Srinivas and Kimura, Daiki and Murugesan, Keerthiram and Fernandez Astudillo, Ram{\'o}n and Naseem, Tahira and Kapanipathi, Pavan and Gray, Alexander

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7100--7110

Abstractive summarization models often produce factually inconsistent summaries that are not supported by the original article. Recently, a number of fact-consistent evaluation techniques have been proposed to address this issue; however, a detailed analysis of how these metrics agree with one another has yet to be conducted. In this paper, we present X-FACTOR, a cross-evaluation of three high-performing fact-aware abstractive summarization methods. First, we show that summarization models are often fine-tuned on datasets that contain factually inconsistent summaries and propose a fact-aware filtering mechanism that improves the quality of training data and, consequently, the factuality of these models. Second, we propose a corrector module that can be used to improve the factual consistency of generated summaries. Third, we present a re-ranking technique that samples summary instances from the output distribution of a summarization model and re-ranks the sampled instances based on their factuality. Finally, we provide a detailed cross-metric agreement analysis that shows how tuning a model to output summaries based on a particular factuality metric influences factuality as determined by the other metrics. Our goal in this work is to facilitate research that improves the factuality and faithfulness of abstractive summarization models.

null

10.18653/v1/2022.emnlp-main.478

null

27,608

inproceedings

wang-etal-2022-paratag

{P}ara{T}ag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, {NLG} Evaluation, and Data Augmentation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.479/

Wang, Shuohang and Xu, Ruochen and Liu, Yang and Zhu, Chenguang and Zeng, Michael

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7111--7122

Paraphrase identification has been formulated as a binary classification task to decide whether two sentences hold a paraphrase relationship. Existing paraphrase datasets only annotate a binary label for each sentence pair. However, after a systematical analysis of existing paraphrase datasets, we found that the degree of paraphrase cannot be well characterized by a single binary label. And the criteria of paraphrase are not even consistent within the same dataset. We hypothesize that such issues would limit the effectiveness of paraphrase models trained on these data. To this end, we propose a novel fine-grained paraphrase annotation schema that labels the minimum spans of tokens in a sentence that don`t have the corresponding paraphrases in the other sentence. Under this setting, we frame paraphrasing as a sequence tagging task. We collect 30k sentence pairs in English with the new annotation schema, resulting in the ParaTag dataset. In addition to reporting baseline results on ParaTag using state-of-art language models, we show that ParaTag is especially useful for training an automatic scorer for language generation evaluation. Finally, we train a paraphrase generation model from ParaTag and achieve better data augmentation performance on the GLUE benchmark than other public paraphrasing datasets.

null

10.18653/v1/2022.emnlp-main.479

null

27,609

inproceedings

nishino-etal-2022-factual

Factual Accuracy is not Enough: Planning Consistent Description Order for Radiology Report Generation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.480/

Nishino, Toru and Miura, Yasuhide and Taniguchi, Tomoki and Ohkuma, Tomoko and Suzuki, Yuki and Kido, Shoji and Tomiyama, Noriyuki

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7123--7138

Radiology report generation systems have the potential to reduce the workload of radiologists by automatically describing the findings in medical images.To broaden the application of the report generation system, the system should generate reports that are not only factually accurate but also chronologically consistent, describing images that are presented in time order, that is, the correct order.We employ a planning-based radiology report generation system that generates the overall structure of reports as {\textquotedblleft}plans'{\textquotedblright} prior to generating reports that are accurate and consistent in order.Additionally, we propose a novel reinforcement learning and inference method, Coordinated Planning (CoPlan), that includes a content planner and a text generator to train and infer in a coordinated manner to alleviate the cascading of errors that are often inherent in planning-based models.We conducted experiments with single-phase diagnostic reports in which the factual accuracy is critical and multi-phase diagnostic reports in which the description order is critical.Our proposed CoPlan improves the content order score by 5.1 pt in time series critical scenarios and the clinical factual accuracy F-score by 9.1 pt in time series irrelevant scenarios, compared those of the baseline models without CoPlan.

null

10.18653/v1/2022.emnlp-main.480

null

27,610

inproceedings

chakrabarty-etal-2022-flute

{FLUTE}: Figurative Language Understanding through Textual Explanations

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.481/

Chakrabarty, Tuhin and Saakyan, Arkadiy and Ghosh, Debanjan and Muresan, Smaranda

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7139--7159

Figurative language understanding has been recently framed as a recognizing textual entailment (RTE) task (a.k.a. natural language inference (NLI)). However, similar to classical RTE/NLI datasets they suffer from spurious correlations and annotation artifacts. To tackle this problem, work on NLI has built explanation-based datasets such as eSNLI, allowing us to probe whether language models are right for the right reasons. Yet no such data exists for figurative language, making it harder to assess genuine understanding of such expressions. To address this issue, we release FLUTE, a dataset of 9,000 figurative NLI instances with explanations, spanning four categories: Sarcasm, Simile, Metaphor, and Idioms. We collect the data through a Human-AI collaboration framework based on GPT-3, crowd workers, and expert annotators. We show how utilizing GPT-3 in conjunction with human annotators (novices and experts) can aid in scaling up the creation of datasets even for such complex linguistic phenomena as figurative language. The baseline performance of the T5 model fine-tuned on FLUTE shows that our dataset can bring us a step closer to developing models that understand figurative language through textual explanations.

null

10.18653/v1/2022.emnlp-main.481

null

27,611

inproceedings

wu-etal-2022-precisely

Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.482/

Wu, Wenhao and Li, Wei and Liu, Jiachen and Xiao, Xinyan and Li, Sujian and Lyu, Yajuan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7160--7176

Though model robustness has been extensively studied in language understanding, the robustness of Seq2Seq generation remains understudied.In this paper, we conduct the first quantitative analysis on the robustness of pre-trained Seq2Seq models. We find that even current SOTA pre-trained Seq2Seq model (BART) is still vulnerable, which leads to significant degeneration in faithfulness and informativeness for text generation tasks.This motivated us to further propose a novel adversarial augmentation framework, namely AdvSeq, for generally improving faithfulness and informativeness of Seq2Seq models via enhancing their robustness. AdvSeq automatically constructs two types of adversarial augmentations during training, including implicit adversarial samples by perturbing word representations and explicit adversarial samples by word swapping, both of which effectively improve Seq2Seq robustness.Extensive experiments on three popular text generation tasks demonstrate that AdvSeq significantly improves both the faithfulness and informativeness of Seq2Seq generation under both automatic and human evaluation settings.

null

10.18653/v1/2022.emnlp-main.482

null

27,612

inproceedings

liu-etal-2022-rlet

{RLET}: A Reinforcement Learning Based Approach for Explainable {QA} with Entailment Trees

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.483/

Liu, Tengxiao and Guo, Qipeng and Hu, Xiangkun and Zhang, Yue and Qiu, Xipeng and Zhang, Zheng

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7177--7189

Interpreting the reasoning process from questions to answers poses a challenge in approaching explainable QA. A recently proposed structured reasoning format, entailment tree, manages to offer explicit logical deductions with entailment steps in a tree structure. To generate entailment trees, prior single pass sequence-to-sequence models lack visible internal decision probability, while stepwise approaches are supervised with extracted single step data and cannot model the tree as a whole. In this work, we propose RLET, a Reinforcement Learning based Entailment Tree generation framework, which is trained utilising the cumulative signals across the whole tree. RLET iteratively performs single step reasoning with sentence selection and deduction generation modules, from which the training signal is accumulated across the tree with elaborately designed aligned reward function that is consistent with the evaluation. To the best of our knowledge, we are the first to introduce RL into the entailment tree generation task. Experiments on three settings of the EntailmentBank dataset demonstrate the strength of using RL framework.

null

10.18653/v1/2022.emnlp-main.483

null

27,613

inproceedings

chemmengath-etal-2022-cat

Let the {CAT} out of the bag: Contrastive Attributed explanations for Text

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.484/

Chemmengath, Saneem and Azad, Amar Prakash and Luss, Ronny and Dhurandhar, Amit

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7190--7206

Contrastive explanations for understanding the behavior of black box models has gained a lot of attention recently as they provide potential for recourse. In this paper, we propose a method Contrastive Attributed explanations for Text (CAT) which provides contrastive explanations for natural language text data with a novel twist as we build and exploit attribute classifiers leading to more semantically meaningful explanations. To ensure that our contrastive generated text has the fewest possible edits with respect to the original text, while also being fluent and close to a human generated contrastive, we resort to a minimal perturbation approach regularized using a BERT language model and attribute classifiers trained on available attributes. We show through qualitative examples and a user study that our method not only conveys more insight because of these attributes, but also leads to better quality (contrastive) text. Quantitatively, we show that our method outperforms other state-of-the-art methods across four data sets on four benchmark metrics.

null

10.18653/v1/2022.emnlp-main.484

null

27,614

inproceedings

kongyoung-etal-2022-monoqa

mono{QA}: Multi-Task Learning of Reranking and Answer Extraction for Open-Retrieval Conversational Question Answering

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.485/

Kongyoung, Sarawoot and Macdonald, Craig and Ounis, Iadh

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7207--7218

To address the Conversational Question Answering (ORConvQA) task, previous work has considered an effective three-stage architecture, consisting of a retriever, a reranker, and a reader to extract the answers. In order to effectively answer the users' questions, a number of existing approaches have applied multi-task learning, such that the same model is shared between the reranker and the reader. Such approaches also typically tackle reranking and reading as classification tasks. On the other hand, recent text generation models, such as monoT5 and UnifiedQA, have been shown to respectively yield impressive performances in passage reranking and reading. However, no prior work has combined monoT5 and UnifiedQA to share a single text generation model that directly extracts the answers for the users instead of predicting the start/end positions in a retrieved passage. In this paper, we investigate the use of Multi-Task Learning (MTL) to improve performance on the ORConvQA task by sharing the reranker and reader`s learned structure in a generative model. In particular, we propose monoQA, which uses a text generation model with multi-task learning for both the reranker and reader. Our model, which is based on the T5 text generation model, is fine-tuned simultaneously for both reranking (in order to improve the precision of the top retrieved passages) and extracting the answer. Our results on the OR-QuAC and OR-CoQA datasets demonstrate the effectiveness of our proposed model, which significantly outperforms existing strong baselines with improvements ranging from +12.31{\%} to +19.51{\%} in MAP and from +5.70{\%} to +23.34{\%} in F1 on all used test sets.

null

10.18653/v1/2022.emnlp-main.485

null

27,615

inproceedings

song-2022-composing

Composing Ci with Reinforced Non-autoregressive Text Generation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.486/

Song, Yan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7219--7229

Composing Ci (also widely known as Song Ci), a special type of classical Chinese poetry, requires to follow particular format once their tune patterns are given. To automatically generate a well-formed Ci, text generation systems should strictly take into account pre-defined rigid formats (e.g., length and rhyme). Yet, most existing approaches regard Ci generation as a conventional sequence-to-sequence task and use autoregressive models, while it is challenging for such models to properly handle the constraints (according to tune patterns) of Ci during the generation process. Moreover, consider that with the format prepared, Ci generation can be operated by an efficient synchronous process, where autoregressive models are limited in doing so since they follow the character-by-character generation protocol. Therefore, in this paper, we propose to compose Ci through a non-autoregressive approach, which not only ensure that the generation process accommodates tune patterns by controlling the rhythm and essential meaning of each sentence, but also allow the model to perform synchronous generation. In addition, we further improve our approach by applying reinforcement learning to the generation process with the rigid constraints of Ci as well as the diversity in content serving as rewards, so as to further maintain the format and content requirement. Experiments on a collected Ci dataset confirm that our proposed approach outperforms strong baselines and previous studies in terms of both automatic evaluation metrics and human judgements.

null

10.18653/v1/2022.emnlp-main.486

null

27,616

inproceedings

xia-etal-2022-metatkg

{M}eta{TKG}: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.487/

Xia, Yuwei and Zhang, Mengqi and Liu, Qiang and Wu, Shu and Zhang, Xiao-Yu

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7230--7240

Reasoning over Temporal Knowledge Graphs (TKGs) aims to predict future facts based on given history. One of the key challenges for prediction is to learn the evolution of facts. Most existing works focus on exploring evolutionary information in history to obtain effective temporal embeddings for entities and relations, but they ignore the variation in evolution patterns of facts, which makes them struggle to adapt to future data with different evolution patterns. Moreover, new entities continue to emerge along with the evolution of facts over time. Since existing models highly rely on historical information to learn embeddings for entities, they perform poorly on such entities with little historical information. To tackle these issues, we propose a novel Temporal Meta-learning framework for TKG reasoning, MetaTKG for brevity. Specifically, our method regards TKG prediction as many temporal meta-tasks, and utilizes the designed Temporal Meta-learner to learn evolutionary meta-knowledge from these meta-tasks. The proposed method aims to guide the backbones to learn to adapt quickly to future data and deal with entities with little historical information by the learned meta-knowledge. Specially, in temporal meta-learner, we design a Gating Integration module to adaptively establish temporal correlations between meta-tasks. Extensive experiments on four widely-used datasets and three backbones demonstrate that our method can greatly improve the performance.

null

10.18653/v1/2022.emnlp-main.487

null

27,617

inproceedings

li-etal-2022-mplug

m{PLUG}: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.488/

Li, Chenliang and Xu, Haiyang and Tian, Junfeng and Wang, Wei and Yan, Ming and Bi, Bin and Ye, Jiabo and Chen, He and Xu, Guohai and Cao, Zheng and Zhang, Ji and Huang, Songfang and Huang, Fei and Zhou, Jingren and Si, Luo

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7241--7259

Large-scale pre-trained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks. This paper presents mPLUG, a new vision-language foundation model for both cross-modal understanding and generation. Most existing pre-trained models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment. To address both problems, mPLUG introduces an effective and efficient vision-language architecture with novel cross-modal skip-connections.mPLUG is pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. It achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering. mPLUG also demonstrates strong zero-shot transferability on vision-language and video-language tasks. The code and pre-trained models are available at https://github.com/alibaba/AliceMind

null

10.18653/v1/2022.emnlp-main.488

null

27,618

inproceedings

tian-etal-2022-q

{Q}-{TOD}: A Query-driven Task-oriented Dialogue System

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.489/

Tian, Xin and Lin, Yingzhan and Song, Mengfei and Bao, Siqi and Wang, Fan and He, Huang and Sun, Shuqi and Wu, Hua

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7260--7271

Existing pipelined task-oriented dialogue systems usually have difficulties adapting to unseen domains, whereas end-to-end systems are plagued by large-scale knowledge bases in practice. In this paper, we introduce a novel query-driven task-oriented dialogue system, namely Q-TOD. The essential information from the dialogue context is extracted into a query, which is further employed to retrieve relevant knowledge records for response generation. Firstly, as the query is in the form of natural language and not confined to the schema of the knowledge base, the issue of domain adaption is alleviated remarkably in Q-TOD. Secondly, as the query enables the decoupling of knowledge retrieval from the generation, Q-TOD gets rid of the issue of knowledge base scalability. To evaluate the effectiveness of the proposed Q-TOD, we collect query annotations for three publicly available task-oriented dialogue datasets. Comprehensive experiments verify that Q-TOD outperforms strong baselines and establishes a new state-of-the-art performance on these datasets.

null

10.18653/v1/2022.emnlp-main.489

null

27,619

inproceedings

liu-etal-2022-dial2vec

Dial2vec: Self-Guided Contrastive Learning of Unsupervised Dialogue Embeddings

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.490/

Liu, Che and Wang, Rui and Jiang, Junfeng and Li, Yongbin and Huang, Fei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7272--7282

In this paper, we introduce the task of learning unsupervised dialogue embeddings.Trivial approaches such as combining pre-trained word or sentence embeddings and encoding through pre-trained language models (PLMs) have been shown to be feasible for this task.However, these approaches typically ignore the conversational interactions between interlocutors, resulting in poor performance.To address this issue, we proposed a self-guided contrastive learning approach named dial2vec.Dial2vec considers a dialogue as an information exchange process.It captures the interaction patterns between interlocutors and leverages them to guide the learning of the embeddings corresponding to each interlocutor.Then the dialogue embedding is obtained by an aggregation of the embeddings from all interlocutors.To verify our approach, we establish a comprehensive benchmark consisting of six widely-used dialogue datasets.We consider three evaluation tasks: domain categorization, semantic relatedness, and dialogue retrieval.Dial2vec achieves on average 8.7, 9.0, and 13.8 points absolute improvements in terms of purity, Spearman`s correlation, and mean average precision (MAP) over the strongest baseline on the three tasks respectively.Further analysis shows that dial2vec obtains informative and discriminative embeddings for both interlocutors under the guidance of the conversational interactions and achieves the best performance when aggregating them through the interlocutor-level pooling strategy.All codes and data are publicly available at https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/dial2vec.

null

10.18653/v1/2022.emnlp-main.490

null

27,620

inproceedings

xie-etal-2022-wr

{WR}-{O}ne2{S}et: Towards Well-Calibrated Keyphrase Generation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.491/

Xie, Binbin and Wei, Xiangpeng and Yang, Baosong and Lin, Huan and Xie, Jun and Wang, Xiaoli and Zhang, Min and Su, Jinsong

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7283--7293

Keyphrase generation aims to automatically generate short phrases summarizing an input document. The recently emerged ONE2SET paradigm (Ye et al., 2021) generates keyphrases as a set and has achieved competitive performance. Nevertheless, we observe serious calibration errors outputted by ONE2SET, especially in the over-estimation of {\ensuremath{\varnothing}} token (means {\textquotedblleft}no corresponding keyphrase{\textquotedblright}). In this paper, we deeply analyze this limitation and identify two main reasons behind: 1) the parallel generation has to introduce excessive {\ensuremath{\varnothing}} as padding tokens into training instances; and 2) the training mechanism assigning target to each slot is unstable and further aggravates the {\ensuremath{\varnothing}} token over-estimation. To make the model well-calibrated, we propose WR-ONE2SET which extends ONE2SET with an adaptive instance-level cost Weighting strategy and a target Re-assignment mechanism. The former dynamically penalizes the over-estimated slots for different instances thus smoothing the uneven training distribution. The latter refines the original inappropriate assignment and reduces the supervisory signals of over-estimated slots. Experimental results on commonly-used datasets demonstrate the effectiveness and generality of our proposed paradigm.

null

10.18653/v1/2022.emnlp-main.491

null

27,621

inproceedings

muradoglu-hulden-2022-eeny

Eeny, meeny, miny, moe. How to choose data for morphological inflection.

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.492/

Muradoglu, Saliha and Hulden, Mans

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7294--7303

Data scarcity is a widespread problem for numerous natural language processing (NLP) tasks within low-resource languages. Within morphology, the labour-intensive task of tagging/glossing data is a serious bottleneck for both NLP and fieldwork. Active learning (AL) aims to reduce the cost of data annotation by selecting data that is most informative for the model. In this paper, we explore four sampling strategies for the task of morphological inflection using a Transformer model: a pair of oracle experiments where data is chosen based on correct/incorrect predictions by the model, model confidence, entropy, and random selection. We investigate the robustness of each sampling strategy across 30 typologically diverse languages, as well as a 10-cycle iteration using Nat{\"ugu as a case study. Our results show a clear benefit to selecting data based on model confidence. Unsurprisingly, the oracle experiment, which is presented as a proxy for linguist/language informer feedback, shows the most improvement. This is followed closely by low-confidence and high-entropy forms. We also show that despite the conventional wisdom of larger data sets yielding better accuracy, introducing more instances of high-confidence, low-entropy, or forms that the model can already inflect correctly, can reduce model performance.

null

10.18653/v1/2022.emnlp-main.492

null

27,622

inproceedings

mei-etal-2022-adaptive

An Adaptive Logical Rule Embedding Model for Inductive Reasoning over Temporal Knowledge Graphs

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.493/

Mei, Xin and Yang, Libin and Cai, Xiaoyan and Jiang, Zuowei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7304--7316

Temporal knowledge graphs (TKGs) extrapolation reasoning predicts future events based on historical information, which has great research significance and broad application value. Existing methods can be divided into embedding-based methods and logical rule-based methods. Embedding-based methods rely on learned entity and relation embeddings to make predictions and thus lack interpretability. Logical rule-based methods bring scalability problems due to being limited by the learned logical rules. We combine the two methods to capture deep causal logic by learning rule embeddings, and propose an interpretable model for temporal knowledge graph reasoning called adaptive logical rule embedding model for inductive reasoning (ALRE-IR). ALRE-IR can adaptively extract and assess reasons contained in historical events, and make predictions based on causal logic. Furthermore, we propose a one-class augmented matching loss for optimization. When evaluated on the ICEWS14, ICEWS0515 and ICEWS18 datasets, the performance of ALRE-IR outperforms other state-of-the-art baselines. The results also demonstrate that ALRE-IR still shows outstanding performance when transferred to related dataset with common relation vocabulary, indicating our proposed model has good zero-shot reasoning ability.

null

10.18653/v1/2022.emnlp-main.493

null

27,623

inproceedings

mou-etal-2022-uninl

{U}ni{NL}: Aligning Representation Learning with Scoring Function for {OOD} Detection via Unified Neighborhood Learning

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.494/

Mou, Yutao and Wang, Pei and He, Keqing and Wu, Yanan and Wang, Jingang and Wu, Wei and Xu, Weiran

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7317--7325

Detecting out-of-domain (OOD) intents from user queries is essential for avoiding wrong operations in task-oriented dialogue systems. The key challenge is how to distinguish in-domain (IND) and OOD intents. Previous methods ignore the alignment between representation learning and scoring function, limiting the OOD detection performance. In this paper, we propose a unified neighborhood learning framework (UniNL) to detect OOD intents. Specifically, we design a KNCL objective for representation learning, and introduce a KNN-based scoring function for OOD detection. We aim to align representation learning with scoring function. Experiments and analysis on two benchmark datasets show the effectiveness of our method.

null

10.18653/v1/2022.emnlp-main.494

null

27,624

inproceedings

marrese-taylor-etal-2022-open

Open-domain Video Commentary Generation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.495/

Marrese-Taylor, Edison and Hamazono, Yumi and Ishigaki, Tatsuya and Topi{\'c}, Goran and Miyao, Yusuke and Kobayashi, Ichiro and Takamura, Hiroya

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7326--7339

Live commentary plays an important role in sports broadcasts and video games, making spectators more excited and immersed. In this context, though approaches for automatically generating such commentary have been proposed in the past, they have been generally concerned with specific fields, where it is possible to leverage domain-specific information. In light of this, we propose the task of generating video commentary in an open-domain fashion. We detail the construction of a new large-scale dataset of transcribed commentary aligned with videos containing various human actions in a variety of domains, and propose approaches based on well-known neural architectures to tackle the task. To understand the strengths and limitations of current approaches, we present an in-depth empirical study based on our data. Our results suggest clear trade-offs between textual and visual inputs for the models and highlight the importance of relying on external knowledge in this open-domain setting, resulting in a set of robust baselines for our task.

null

10.18653/v1/2022.emnlp-main.495

null

27,625

inproceedings

senge-etal-2022-one

One size does not fit all: Investigating strategies for differentially-private learning across {NLP} tasks

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.496/

Senge, Manuel and Igamberdiev, Timour and Habernal, Ivan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7340--7353

Preserving privacy in contemporary NLP models allows us to work with sensitive data, but unfortunately comes at a price. We know that stricter privacy guarantees in differentially-private stochastic gradient descent (DP-SGD) generally degrade model performance. However, previous research on the efficiency of DP-SGD in NLP is inconclusive or even counter-intuitive. In this short paper, we provide an extensive analysis of different privacy preserving strategies on seven downstream datasets in five different {\textquoteleft}typical' NLP tasks with varying complexity using modern neural models based on BERT and XtremeDistil architectures. We show that unlike standard non-private approaches to solving NLP tasks, where bigger is usually better, privacy-preserving strategies do not exhibit a winning pattern, and each task and privacy regime requires a special treatment to achieve adequate performance.

null

10.18653/v1/2022.emnlp-main.496

null

27,626

inproceedings

liu-etal-2022-counterfactual

Counterfactual Recipe Generation: Exploring Compositional Generalization in a Realistic Scenario

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.497/

Liu, Xiao and Feng, Yansong and Tang, Jizhi and Hu, Chengang and Zhao, Dongyan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7354--7370

People can acquire knowledge in an unsupervised manner by reading, and compose the knowledge to make novel combinations. In this paper, we investigate whether pretrained language models can perform compositional generalization in a realistic setting: recipe generation. We design the counterfactual recipe generation task, which asks models to modify a base recipe according to the change of an ingredient. This task requires compositional generalization at two levels: the surface level of incorporating the new ingredient into the base recipe, and the deeper level of adjusting actions related to the changing ingredient. We collect a large-scale recipe dataset in Chinese for models to learn culinary knowledge, and a subset of action-level fine-grained annotations for evaluation.We finetune pretrained language models on the recipe corpus, and use unsupervised counterfactual generation methods to generate modified recipes.Results show that existing models have difficulties in modifying the ingredients while preserving the original text style, and often miss actions that need to be adjusted. Although pretrained language models can generate fluent recipe texts, they fail to truly learn and use the culinary knowledge in a compositional way. Code and data are available at https://github.com/xxxiaol/counterfactual-recipe-generation.

null

10.18653/v1/2022.emnlp-main.497

null

27,627

inproceedings

kim-etal-2022-tutoring

Tutoring Helps Students Learn Better: Improving Knowledge Distillation for {BERT} with Tutor Network

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.498/

Kim, Junho and Park, Jun-Hyung and Lee, Mingyu and Mok, Wing-Lam and Choi, Joon-Young and Lee, SangKeun

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7371--7382

Pre-trained language models have achieved remarkable successes in natural language processing tasks, coming at the cost of increasing model size. To address this issue, knowledge distillation (KD) has been widely applied to compress language models. However, typical KD approaches for language models have overlooked the difficulty of training examples, suffering from incorrect teacher prediction transfer and sub-efficient training. In this paper, we propose a novel KD framework, Tutor-KD, which improves the distillation effectiveness by controlling the difficulty of training examples during pre-training. We introduce a tutor network that generates samples that are easy for the teacher but difficult for the student, with training on a carefully designed policy gradient method. Experimental results show that Tutor-KD significantly and consistently outperforms the state-of-the-art KD methods with variously sized student models on the GLUE benchmark, demonstrating that the tutor can effectively generate training examples for the student.

null

10.18653/v1/2022.emnlp-main.498

null

27,628

inproceedings

artetxe-etal-2022-corpus

Does Corpus Quality Really Matter for Low-Resource Languages?

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.499/

Artetxe, Mikel and Aldabe, Itziar and Agerri, Rodrigo and Perez-de-Vi{\~n}aspre, Olatz and Soroa, Aitor

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7383--7390

The vast majority of non-English corpora are derived from automatically filtered versions of CommonCrawl. While prior work has identified major issues on the quality of these datasets (Kreutzer et al., 2021), it is not clear how this impacts downstream performance. Taking representation learning in Basque as a case study, we explore tailored crawling (manually identifying and scraping websites with high-quality content) as an alternative to filtering CommonCrawl. Our new corpus, called EusCrawl, is similar in size to the Basque portion of popular multilingual corpora like CC100 and mC4, yet it has a much higher quality according to native annotators. For instance, 66{\%} of documents are rated as high-quality for EusCrawl, in contrast with {\ensuremath{<}}33{\%} for both mC4 and CC100. Nevertheless, we obtain similar results on downstream NLU tasks regardless of the corpus used for pre-training. Our work suggests that NLU performance in low-resource languages is not primarily constrained by the quality of the data, and other factors like corpus size and domain coverage can play a more important role.

null

10.18653/v1/2022.emnlp-main.499

null

27,629

inproceedings

plepi-etal-2022-unifying

Unifying Data Perspectivism and Personalization: An Application to Social Norms

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.500/

Plepi, Joan and Neuendorf, B{\'e}la and Flek, Lucie and Welch, Charles

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7391--7402

Instead of using a single ground truth for language processing tasks, several recent studies have examined how to represent and predict the labels of the set of annotators. However, often little or no information about annotators is known, or the set of annotators is small. In this work, we examine a corpus of social media posts about conflict from a set of 13k annotators and 210k judgements of social norms. We provide a novel experimental setup that applies personalization methods to the modeling of annotators and compare their effectiveness for predicting the perception of social norms. We further provide an analysis of performance across subsets of social situations that vary by the closeness of the relationship between parties in conflict, and assess where personalization helps the most.

null

10.18653/v1/2022.emnlp-main.500

null

27,630

inproceedings

ross-etal-2022-self

Does Self-Rationalization Improve Robustness to Spurious Correlations?

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.501/

Ross, Alexis and Peters, Matthew and Marasovic, Ana

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7403--7416

Rationalization is fundamental to human reasoning and learning. NLP models trained to produce rationales along with predictions, called self-rationalization models, have been investigated for their interpretability and utility to end-users. However, the extent to which training with human-written rationales facilitates learning remains an under-explored question. We ask whether training models to self-rationalize can aid in their learning to solve tasks for the right reasons. Specifically, we evaluate how training self-rationalization models with free-text rationales affects robustness to spurious correlations in fine-tuned encoder-decoder and decoder-only models of six different sizes. We evaluate robustness to spurious correlations by measuring performance on 1) manually annotated challenge datasets and 2) subsets of original test sets where reliance on spurious correlations would fail to produce correct answers. We find that while self-rationalization can improve robustness to spurious correlations in low-resource settings, it tends to hurt robustness in higher-resource settings. Furthermore, these effects depend on model family and size, as well as on rationale content. Together, our results suggest that explainability can come at the cost of robustness; thus, appropriate care should be taken when training self-rationalizing models with the goal of creating more trustworthy models.

null

10.18653/v1/2022.emnlp-main.501

null

27,631

inproceedings

lee-etal-2022-efficient-pre

Efficient Pre-training of Masked Language Model via Concept-based Curriculum Masking

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.502/

Lee, Mingyu and Park, Jun-Hyung and Kim, Junho and Kim, Kang-Min and Lee, SangKeun

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7417--7427

Self-supervised pre-training has achieved remarkable success in extensive natural language processing tasks. Masked language modeling (MLM) has been widely used for pre-training effective bidirectional representations but comes at a substantial training cost. In this paper, we propose a novel concept-based curriculum masking (CCM) method to efficiently pre-train a language model. CCM has two key differences from existing curriculum learning approaches to effectively reflect the nature of MLM. First, we introduce a novel curriculum that evaluates the MLM difficulty of each token based on a carefully-designed linguistic difficulty criterion. Second, we construct a curriculum that masks easy words and phrases first and gradually masks related ones to the previously masked ones based on a knowledge graph. Experimental results show that CCM significantly improves pre-training efficiency. Specifically, the model trained with CCM shows comparative performance with the original BERT on the General Language Understanding Evaluation benchmark at half of the training cost.

null

10.18653/v1/2022.emnlp-main.502

null

27,632

inproceedings

pelloni-etal-2022-subword

Subword Evenness ({S}u{E}) as a Predictor of Cross-lingual Transfer to Low-resource Languages

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.503/

Pelloni, Olga and Shaitarova, Anastassia and Samardzic, Tanja

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7428--7445

Pre-trained multilingual models, such as mBERT, XLM-R and mT5, are used to improve the performance on various tasks in low-resource languages via cross-lingual transfer. In this framework, English is usually seen as the most natural choice for a transfer language (for fine-tuning or continued training of a multilingual pre-trained model), but it has been revealed recently that this is often not the best choice. The success of cross-lingual transfer seems to depend on some properties of languages, which are currently hard to explain. Successful transfer often happens between unrelated languages and it often cannot be explained by data-dependent factors.In this study, we show that languages written in non-Latin and non-alphabetic scripts (mostly Asian languages) are the best choices for improving performance on the task of Masked Language Modelling (MLM) in a diverse set of 30 low-resource languages and that the success of the transfer is well predicted by our novel measure of Subword Evenness (SuE). Transferring language models over the languages that score low on our measure results in the lowest average perplexity over target low-resource languages. Our correlation coefficients obtained with three different pre-trained multilingual models are consistently higher than all the other predictors, including text-based measures (type-token ratio, entropy) and linguistically motivated choice (genealogical and typological proximity).

null

10.18653/v1/2022.emnlp-main.503

null

27,633

inproceedings

li-etal-2022-unified

A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.504/

Li, Wenbiao and Ziyang, Wang and Wu, Yunfang

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7446--7457

Readability assessment is a basic research task in the field of education. Traditional methods mainly employ machine learning classifiers with hundreds of linguistic features. Although the deep learning model has become the prominent approach for almost all NLP tasks, it is less explored for readability assessment. In this paper, we propose a BERT-based model with feature projection and length-balanced loss (BERT-FP-LBL) to determine the difficulty level of a given text. First, we introduce topic features guided by difficulty knowledge to complement the traditional linguistic features. From the linguistic features, we extract really useful orthogonal features to supplement BERT representations by means of projection filtering. Furthermore, we design a length-balanced loss to handle the greatly varying length distribution of the readability data. We conduct experiments on three English benchmark datasets and one Chinese dataset, and the experimental results show that our proposed model achieves significant improvements over baseline models. Interestingly, our proposed model achieves comparable results with human experts in consistency test.

null

10.18653/v1/2022.emnlp-main.504

null

27,634

inproceedings

du-etal-2022-speaker

Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.505/

Du, Zhihao and Zhang, ShiLiang and Zheng, Siqi and Yan, Zhi-Jie

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7458--7469

Recently, hybrid systems of clustering and neural diarization models have been successfully applied in multi-party meeting analysis. However, current models always treat overlapped speaker diarization as a multi-label classification problem, where speaker dependency and overlaps are not well considered. To overcome the disadvantages, we reformulate overlapped speaker diarization task as a single-label prediction problem via the proposed power set encoding (PSE). Through this formulation, speaker dependency and overlaps can be explicitly modeled. To fully leverage this formulation, we further propose the speaker overlap-aware neural diarization (SOND) model, which consists of a context-independent (CI) scorer to model global speaker discriminability, a context-dependent scorer (CD) to model local discriminability, and a speaker combining network (SCN) to combine and reassign speaker activities. Experimental results show that using the proposed formulation can outperform the state-of-the-art methods based on target speaker voice activity detection, and the performance can be further improved with SOND, resulting in a 6.30{\%} relative diarization error reduction.

null

10.18653/v1/2022.emnlp-main.505

null

27,635

inproceedings

panayotov-etal-2022-greener

{GREENER}: Graph Neural Networks for News Media Profiling

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.506/

Panayotov, Panayot and Shukla, Utsav and Sencar, Husrev Taha and Nabeel, Mohamed and Nakov, Preslav

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7470--7480

We study the problem of profiling news media on the Web with respect to their factuality of reporting and bias. This is an important but under-studied problem related to disinformation and {\textquotedblleft}fake news{\textquotedblright} detection, but it addresses the issue at a coarser granularity compared to looking at an individual article or an individual claim. This is useful as it allows to profile entire media outlets in advance. Unlike previous work, which has focused primarily on text (e.g., on the text of the articles published by the target website, or on the textual description in their social media profiles or in Wikipedia), here our main focus is on modeling the similarity between media outlets based on the overlap of their audience. This is motivated by homophily considerations, i.e., the tendency of people to have connections to people with similar interests, which we extend to media, hypothesizing that similar types of media would be read by similar kinds of users. In particular, we propose GREENER (GRaph nEural nEtwork for News mEdia pRofiling), a model that builds a graph of inter-media connections based on their audience overlap, and then uses graph neural networks to represent each medium. We find that such representations are quite useful for predicting the factuality and the bias of news media outlets, yielding improvements over state-of-the-art results reported on two datasets. When augmented with conventionally used representations obtained from news articles, Twitter, YouTube, Facebook, and Wikipedia, prediction accuracy is found to improve by 2.5-27 macro-F1 points for the two tasks.

null

10.18653/v1/2022.emnlp-main.506

null

27,636

inproceedings

sun-etal-2022-graph

Graph {H}awkes Transformer for Extrapolated Reasoning on Temporal Knowledge Graphs

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.507/

Sun, Haohai and Geng, Shangyi and Zhong, Jialun and Hu, Han and He, Kun

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7481--7493

Temporal Knowledge Graph (TKG) reasoning has attracted increasing attention due to its enormous potential value, and the critical issue is how to model the complex temporal structure information effectively. Recent studies use the method of encoding graph snapshots into hidden vector space and then performing heuristic deductions, which perform well on the task of entity prediction. However, these approaches cannot predict when an event will occur and have the following limitations: 1) there are many facts not related to the query that can confuse the model; 2) there exists information forgetting caused by long-term evolutionary processes. To this end, we propose a Graph Hawkes Transformer (GHT) for both TKG entity prediction and time prediction tasks in the future time. In GHT, there are two variants of Transformer, which capture the instantaneous structural information and temporal evolution information, respectively, and a new relational continuous-time encoding function to facilitate feature evolution with the Hawkes process. Extensive experiments on four public datasets demonstrate its superior performance, especially on long-term evolutionary tasks.

null

10.18653/v1/2022.emnlp-main.507

null

27,637

inproceedings

zhou-etal-2022-unirpg

{U}ni{RPG}: Unified Discrete Reasoning over Table and Text as Program Generation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.508/

Zhou, Yongwei and Bao, Junwei and Duan, Chaoqun and Wu, Youzheng and He, Xiaodong and Zhao, Tiejun

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7494--7507

Question answering requiring discrete reasoning, e.g., arithmetic computing, comparison, and counting, over knowledge is a challenging task.In this paper, we propose UniRPG, a semantic-parsing-based approach advanced in interpretability and scalability, to perform Unified discrete Reasoning over heterogeneous knowledge resources, i.e., table and text, as Program Generation. Concretely, UniRPG consists of a neural programmer and a symbolic program executor,where a program is the composition of a set of pre-defined general atomic and higher-order operations and arguments extracted from table and text.First, the programmer parses a question into a program by generating operations and copying arguments, and then, the executor derives answers from table and text based on the program.To alleviate the costly program annotation issue, we design a distant supervision approach for programmer learning, where pseudo programs are automatically constructed without annotated derivations.Extensive experiments on the TAT-QA dataset show that UniRPG achieves tremendous improvements and enhances interpretability and scalability compared with previous state-of-the-art methods, even without derivation annotation.Moreover, it achieves promising performance on the textual dataset DROP without derivation annotation.

null

10.18653/v1/2022.emnlp-main.508

null

27,638

inproceedings

van-de-kar-etal-2022-dont

Don`t Prompt, Search! Mining-based Zero-Shot Learning with Language Models

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.509/

van de Kar, Mozes and Xia, Mengzhou and Chen, Danqi and Artetxe, Mikel

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7508--7520

Masked language models like BERT can perform text classification in a zero-shot fashion by reformulating downstream tasks as text infilling. However, this approach is highly sensitive to the template used to prompt the model, yet practitioners are blind when designing them in strict zero-shot settings. In this paper, we propose an alternative mining-based approach for zero-shot learning. Instead of prompting language models, we use regular expressions to mine labeled examples from unlabeled corpora, which can optionally be filtered through prompting, and used to finetune a pretrained model. Our method is more flexible and interpretable than prompting, and outperforms it on a wide range of tasks when using comparable templates. Our results suggest that the success of prompting can partly be explained by the model being exposed to similar examples during pretraining, which can be directly retrieved through regular expressions.

null

10.18653/v1/2022.emnlp-main.509

null

27,639

inproceedings

wang-etal-2022-semgraph

{SEMG}raph: Incorporating Sentiment Knowledge and Eye Movement into Graph Model for Sentiment Analysis

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.510/

Wang, Bingbing and Liang, Bin and Du, Jiachen and Yang, Min and Xu, Ruifeng

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7521--7531

This paper investigates the sentiment analysis task from a novel perspective by incorporating sentiment knowledge and eye movement into a graph architecture, aiming to draw the eye movement-based sentiment relationships for learning the sentiment expression of the context. To be specific, we first explore a linguistic probing eye movement paradigm to extract eye movement features based on the close relationship between linguistic features and the early and late processes of human reading behavior. Furthermore, to derive eye movement features with sentiment concepts, we devise a novel weighting strategy to integrate sentiment scores extracted from affective commonsense knowledge into eye movement features, called sentiment-eye movement weights. Then, the sentiment-eye movement weights are exploited to build the sentiment-eye movement guided graph (SEMGraph) model, so as to model the intricate sentiment relationships in the context. Experimental results on two sentiment analysis datasets with eye movement signals and three sentiment analysis datasets without eye movement signals show that the proposed SEMGraph achieves state-of-the-art performance, and can also be directly generalized to those sentiment analysis datasets without eye movement signals.

null

10.18653/v1/2022.emnlp-main.510

null

27,640

inproceedings

espla-gomis-etal-2022-cross

Cross-lingual neural fuzzy matching for exploiting target-language monolingual corpora in computer-aided translation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.511/

Espl{\`a}-Gomis, Miquel and S{\'a}nchez-Cartagena, V{\'i}ctor M. and P{\'e}rez-Ortiz, Juan Antonio and S{\'a}nchez-Mart{\'i}nez, Felipe

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7532--7543

Computer-aided translation (CAT) tools based on translation memories (MT) play a prominent role in the translation workflow of professional translators. However, the reduced availability of in-domain TMs, as compared to in-domain monolingual corpora, limits its adoption for a number of translation tasks. In this paper, we introduce a novel neural approach aimed at overcoming this limitation by exploiting not only TMs, but also in-domain target-language (TL) monolingual corpora, and still enabling a similar functionality to that offered by conventional TM-based CAT tools. Our approach relies on cross-lingual sentence embeddings to retrieve translation proposals from TL monolingual corpora, and on a neural model to estimate their post-editing effort. The paper presents an automatic evaluation of these techniques on four language pairs that shows that our approach can successfully exploit monolingual texts in a TM-based CAT environment, increasing the amount of useful translation proposals, and that our neural model for estimating the post-editing effort enables the combination of translation proposals obtained from monolingual corpora and from TMs in the usual way. A human evaluation performed on a single language pair confirms the results of the automatic evaluation and seems to indicate that the translation proposals retrieved with our approach are more useful than what the automatic evaluation shows.

null

10.18653/v1/2022.emnlp-main.511

null

27,641

inproceedings

vulic-etal-2022-multi

Multi-Label Intent Detection via Contrastive Task Specialization of Sentence Encoders

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.512/

Vuli{\'c}, Ivan and Casanueva, I{\~n}igo and Spithourakis, Georgios and Mondal, Avishek and Wen, Tsung-Hsien and Budzianowski, Pawe{\l}

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7544--7559

Deploying task-oriented dialog ToD systems for new domains and tasks requires natural language understanding models that are 1) resource-efficient and work under low-data regimes; 2) adaptable, efficient, and quick-to-train; 3) expressive and can handle complex ToD scenarios with multiple user intents in a single utterance. Motivated by these requirements, we introduce a novel framework for multi-label intent detection (mID): MultI-ConvFiT (Multi-Label Intent Detection via Contrastive Conversational Fine-Tuning). While previous work on efficient single-label intent detection learns a classifier on top of a fixed sentence encoder (SE), we propose to 1) transform general-purpose SEs into task-specialized SEs via contrastive fine-tuning on annotated multi-label data, 2) where task specialization knowledge can be stored into lightweight adapter modules without updating the original parameters of the input SE, and then 3) we build improved mID classifiers stacked on top of fixed specialized SEs. Our main results indicate that MultI-ConvFiT yields effective mID models, with large gains over non-specialized SEs reported across a spectrum of different mID datasets, both in low-data and high-data regimes.

null

10.18653/v1/2022.emnlp-main.512

null

27,642

inproceedings

foroutan-etal-2022-discovering

Discovering Language-neutral Sub-networks in Multilingual Language Models

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.513/

Foroutan, Negar and Banaei, Mohammadreza and Lebret, R{\'e}mi and Bosselut, Antoine and Aberer, Karl

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7560--7575

Multilingual pre-trained language models transfer remarkably well on cross-lingual downstream tasks. However, the extent to which they learn language-neutral representations (i.e., shared representations that encode similar phenomena across languages), and the effect of such representations on cross-lingual transfer performance, remain open questions.In this work, we conceptualize language neutrality of multilingual models as a function of the overlap between language-encoding sub-networks of these models. We employ the lottery ticket hypothesis to discover sub-networks that are individually optimized for various languages and tasks. Our evaluation across three distinct tasks and eleven typologically-diverse languages demonstrates that sub-networks for different languages are topologically similar (i.e., language-neutral), making them effective initializations for cross-lingual transfer with limited performance degradation.

null

10.18653/v1/2022.emnlp-main.513

null

27,643

inproceedings

yang-etal-2022-parameter

Parameter-Efficient Tuning Makes a Good Classification Head

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.514/

Yang, Zhuoyi and Ding, Ming and Guo, Yanhui and Lv, Qingsong and Tang, Jie

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7576--7586

In recent years, pretrained models revolutionized the paradigm of natural language understanding (NLU), where we append a randomly initialized classification head after the pretrained backbone, e.g. BERT, and finetune the whole model. As the pretrained backbone makes a major contribution to the improvement, we naturally expect a good pretrained classification head can also benefit the training. However, the final-layer output of the backbone, i.e. the input of the classification head, will change greatly during finetuning, making the usual head-only pretraining ineffective. In this paper, we find that parameter-efficient tuning makes a good classification head, with which we can simply replace the randomly initialized heads for a stable performance gain. Our experiments demonstrate that the classification head jointly pretrained with parameter-efficient tuning consistently improves the performance on 9 tasks in GLUE and SuperGLUE.

null

10.18653/v1/2022.emnlp-main.514

null

27,644

inproceedings

wu-etal-2022-stgn

{STGN}: an Implicit Regularization Method for Learning with Noisy Labels in Natural Language Processing

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.515/

Wu, Tingting and Ding, Xiao and Tang, Minji and Zhang, Hao and Qin, Bing and Liu, Ting

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7587--7598

Noisy labels are ubiquitous in natural language processing (NLP) tasks. Existing work, namely learning with noisy labels in NLP, is often limited to dedicated tasks or specific training procedures, making it hard to be widely used. To address this issue, SGD noise has been explored to provide a more general way to alleviate the effect of noisy labels by involving benign noise in the process of stochastic gradient descent. However, previous studies exert identical perturbation for all samples, which may cause overfitting on incorrect ones or optimizing correct ones inadequately. To facilitate this, we propose a novel stochastic tailor-made gradient noise (STGN), mitigating the effect of inherent label noise by introducing tailor-made benign noise for each sample. Specifically, we investigate multiple principles to precisely and stably discriminate correct samples from incorrect ones and thus apply different intensities of perturbation to them. A detailed theoretical analysis shows that STGN has good properties, beneficial for model generalization. Experiments on three different NLP tasks demonstrate the effectiveness and versatility of STGN. Also, STGN can boost existing robust training methods.

null

10.18653/v1/2022.emnlp-main.515

null

27,645

inproceedings

zhang-etal-2022-cross

Cross-Modal Similarity-Based Curriculum Learning for Image Captioning

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.516/

Zhang, Hongkuan and Sugawara, Saku and Aizawa, Akiko and Zhou, Lei and Sasano, Ryohei and Takeda, Koichi

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7599--7606

Image captioning models require the high-level generalization ability to describe the contents of various images in words. Most existing approaches treat the image{--}caption pairs equally in their training without considering the differences in their learning difficulties. Several image captioning approaches introduce curriculum learning methods that present training data with increasing levels of difficulty. However, their difficulty measurements are either based on domain-specific features or prior model training. In this paper, we propose a simple yet efficient difficulty measurement for image captioning using cross-modal similarity calculated by a pretrained vision{--}language model. Experiments on the COCO and Flickr30k datasets show that our proposed approach achieves superior performance and competitive convergence speed to baselines without requiring heuristics or incurring additional training costs. Moreover, the higher model performance on difficult examples and unseen data also demonstrates the generalization ability.

null

10.18653/v1/2022.emnlp-main.516

null

27,646

inproceedings

meissner-etal-2022-debiasing

Debiasing Masks: A New Framework for Shortcut Mitigation in {NLU}

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.517/

Meissner, Johannes Mario and Sugawara, Saku and Aizawa, Akiko

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7607--7613

Debiasing language models from unwanted behaviors in Natural Language Understanding (NLU) tasks is a topic with rapidly increasing interest in the NLP community. Spurious statistical correlations in the data allow models to perform shortcuts and avoid uncovering more advanced and desirable linguistic features.A multitude of effective debiasing approaches has been proposed, but flexibility remains a major issue. For the most part, models must be retrained to find a new set of weights with debiased behavior.We propose a new debiasing method in which we identify debiased pruning masks that can be applied to a finetuned model. This enables the selective and conditional application of debiasing behaviors.We assume that bias is caused by a certain subset of weights in the network; our method is, in essence, a mask search to identify and remove biased weights.Our masks show equivalent or superior performance to the standard counterparts, while offering important benefits.Pruning masks can be stored with high efficiency in memory, and it becomes possible to switch among several debiasing behaviors (or revert back to the original biased model) at inference time. Finally, it opens the doors to further research on how biases are acquired by studying the generated masks. For example, we observed that the early layers and attention heads were pruned more aggressively, possibly hinting towards the location in which biases may be encoded.

null

10.18653/v1/2022.emnlp-main.517

null

27,647

inproceedings

lu-etal-2022-extending

Extending Phrase Grounding with Pronouns in Visual Dialogues

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.518/

Lu, Panzhong and Zhang, Xin and Zhang, Meishan and Zhang, Min

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7614--7625

Conventional phrase grounding aims to localize noun phrases mentioned in a given caption to their corresponding image regions, which has achieved great success recently. Apparently, sole noun phrase grounding is not enough for cross-modal visual language understanding. Here we extend the task by considering pronouns as well. First, we construct a dataset of phrase grounding with both noun phrases and pronouns to image regions. Based on the dataset, we test the performance of phrase grounding by using a state-of-the-art literature model of this line. Then, we enhance the baseline grounding model with coreference information which should help our task potentially, modeling the coreference structures with graph convolutional networks. Experiments on our dataset, interestingly, show that pronouns are easier to ground than noun phrases, where the possible reason might be that these pronouns are much less ambiguous. Additionally, our final model with coreference information can significantly boost the grounding performance of both noun phrases and pronouns.

null

10.18653/v1/2022.emnlp-main.518

null

27,648

inproceedings

aumiller-etal-2022-eur

{EUR}-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.519/

Aumiller, Dennis and Chouhan, Ashish and Gertz, Michael

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7626--7639

Existing summarization datasets come with two main drawbacks: (1) They tend to focus on overly exposed domains, such as news articles or wiki-like texts, and (2) are primarily monolingual, with few multilingual datasets.In this work, we propose a novel dataset, called EUR-Lex-Sum, based on manually curated document summaries of legal acts from the European Union law platform (EUR-Lex). Documents and their respective summaries exist as cross-lingual paragraph-aligned data in several of the 24 official European languages, enabling access to various cross-lingual and lower-resourced summarization setups. We obtain up to 1,500 document/summary pairs per language, including a subset of 375 cross-lingually aligned legal acts with texts available in *all* 24 languages. In this work, the data acquisition process is detailed and key characteristics of the resource are compared to existing summarization resources. In particular, we illustrate challenging sub-problems and open questions on the dataset that could help the facilitation of future research in the direction of domain-specific cross-lingual summarization.Limited by the extreme length and language diversity of samples, we further conduct experiments with suitable extractive monolingual and cross-lingual baselines for future work. Code for the extraction as well as access to our data and baselines is available online at: [https://github.com/achouhan93/eur-lex-sum](https://github.com/achouhan93/eur-lex-sum).

null

10.18653/v1/2022.emnlp-main.519

null

27,649

inproceedings

wang-lu-2022-differentiable

Differentiable Data Augmentation for Contrastive Sentence Representation Learning

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.520/

Wang, Tianduo and Lu, Wei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7640--7653

Fine-tuning a pre-trained language model via the contrastive learning framework with a large amount of unlabeled sentences or labeled sentence pairs is a common way to obtain high-quality sentence representations. Although the contrastive learning framework has shown its superiority on sentence representation learning over previous methods, the potential of such a framework is under-explored so far due to the simple method it used to construct positive pairs. Motivated by this, we propose a method that makes hard positives from the original training examples. A pivotal ingredient of our approach is the use of prefix that attached to a pre-trained language model, which allows for differentiable data augmentation during contrastive learning. Our method can be summarized in two steps: supervised prefix-tuning followed by joint contrastive fine-tuning with unlabeled or labeled examples. Our experiments confirm the effectiveness of our data augmentation approach. The proposed method yields significant improvements over existing methods under both semi-supervised and supervised settings. Our experiments under a low labeled data setting also show that our method is more label-efficient than the state-of-the-art contrastive learning methods.

null

10.18653/v1/2022.emnlp-main.520

null

27,650

inproceedings

wang-etal-2022-text

Text Style Transferring via Adversarial Masking and Styled Filling

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.521/

Wang, Jiarui and Zhang, Richong and Chen, Junfan and Kim, Jaein and Mao, Yongyi

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7654--7663

Text style transfer is an important task in natural language processing with broad applications. Existing models following the masking and filling scheme suffer two challenges: the word masking procedure may mistakenly remove unexpected words and the selected words in the word filling procedure may lack diversity and semantic consistency. To tackle both challenges, in this study, we propose a style transfer model, with an adversarial masking approach and a styled filling technique (AMSF). Specifically, AMSF first trains a mask predictor by adversarial training without manual configuration. Then two additional losses, i.e. an entropy maximization loss and a consistency regularization loss, are introduced in training the word filling module to guarantee the diversity and semantic consistency of the transferred texts. Experimental results and analysis on two benchmark text style transfer data sets demonstrate the effectiveness of the proposed approaches.

null

10.18653/v1/2022.emnlp-main.521

null

27,651

inproceedings

liu-etal-2022-character

Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.522/

Liu, Aiwei and Yu, Honghai and Hu, Xuming and Li, Shu{'}ang and Lin, Li and Ma, Fukun and Yang, Yawen and Wen, Lijie

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7664--7676

We propose the first character-level white-box adversarial attack method against transformer models. The intuition of our method comes from the observation that words are split into subtokens before being fed into the transformer models and the substitution between two close subtokens has a similar effect with the character modification. Our method mainly contains three steps. First, a gradient-based method is adopted to find the most vulnerable words in the sentence. Then we split the selected words into subtokens to replace the origin tokenization result from the transformer tokenizer. Finally, we utilize an adversarial loss to guide the substitution of attachable subtokens in which the Gumbel-softmax trick is introduced to ensure gradient propagation.Meanwhile, we introduce the visual and length constraint in the optimization process to achieve minimum character modifications.Extensive experiments on both sentence-level and token-level tasks demonstrate that our method could outperform the previous attack methods in terms of success rate and edit distance. Furthermore, human evaluation verifies our adversarial examples could preserve their origin labels.

null

10.18653/v1/2022.emnlp-main.522

null

27,652

inproceedings

tan-etal-2022-query

Query-based Instance Discrimination Network for Relational Triple Extraction

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.523/

Tan, Zeqi and Shen, Yongliang and Hu, Xuming and Zhang, Wenqi and Cheng, Xiaoxia and Lu, Weiming and Zhuang, Yueting

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7677--7690

Joint entity and relation extraction has been a core task in the field of information extraction. Recent approaches usually consider the extraction of relational triples from a stereoscopic perspective, either learning a relation-specific tagger or separate classifiers for each relation type. However, they still suffer from error propagation, relation redundancy and lack of high-level connections between triples. To address these issues, we propose a novel query-based approach to construct instance-level representations for relational triples. By metric-based comparison between query embeddings and token embeddings, we can extract all types of triples in one step, thus eliminating the error propagation problem. In addition, we learn the instance-level representation of relational triples via contrastive learning. In this way, relational triples can not only enclose rich class-level semantics but also access to high-order global connections. Experimental results show that our proposed method achieves the state of the art on five widely used benchmarks.

null

10.18653/v1/2022.emnlp-main.523

null

27,653

inproceedings

li-etal-2022-learning-inter

Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.524/

Li, Yuling and Yu, Kui and Huang, Xiaoling and Zhang, Yuhong

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7691--7700

Few-shot knowledge graph completion (FKGC) aims to infer unknown fact triples of a relation using its few-shot reference entity pairs. Recent FKGC studies focus on learning semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities. Such practice, however, ignores the inter-entity interaction, resulting in low-discrimination representations for entity pairs, especially when these entity pairs are associated with 1-to-N, N-to-1, and N-to-N relations. To address this issue, this paper proposes a novel FKGC model, named Cross-Interaction Attention Network (CIAN) to investigate the inter-entity interaction between head and tail entities. Specifically, we first explore the interactions within entities by computing the attention between the task relation and each entity neighbor, and then model the interactions between head and tail entities by letting an entity to attend to the neighborhood of its paired entity. In this way, CIAN can figure out the relevant semantics between head and tail entities, thereby generating more discriminative representations for entity pairs. Extensive experiments on two public datasets show that CIAN outperforms several state-of-the-art methods. The source code is available at \url{https://github.com/cjlyl/FKGC-CIAN}.

null

10.18653/v1/2022.emnlp-main.524

null

27,654

inproceedings

sundriyal-etal-2022-empowering

Empowering the Fact-checkers! Automatic Identification of Claim Spans on {T}witter

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.525/

Sundriyal, Megha and Kulkarni, Atharva and Pulastya, Vaibhav and Akhtar, Md. Shad and Chakraborty, Tanmoy

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7701--7715

The widespread diffusion of medical and political claims in the wake of COVID-19 has led to a voluminous rise in misinformation and fake news. The current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of claim-ridden misinformation. However, the rate of information dissemination is such that it vastly outpaces the fact-checkers' strength. Therefore, to aid manual fact-checkers in eliminating the superfluous content, it becomes imperative to automatically identify and extract the snippets of claim-worthy (mis)information present in a post. In this work, we introduce the novel task of Claim Span Identification (CSI). We propose CURT, a large-scale Twitter corpus with token-level claim spans on more than 7.5k tweets. Furthermore, along with the standard token classification baselines, we benchmark our dataset with DABERTa, an adapter-based variation of RoBERTa. The experimental results attest that DABERTa outperforms the baseline systems across several evaluation metrics, improving by about 1.5 points. We also report detailed error analysis to validate the model`s performance along with the ablation studies. Lastly, we release our comprehensive span annotation guidelines for public use.

null

10.18653/v1/2022.emnlp-main.525

null

27,655

inproceedings

wang-etal-2022-clidsum

{C}lid{S}um: A Benchmark Dataset for Cross-Lingual Dialogue Summarization

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.526/

Wang, Jiaan and Meng, Fandong and Lu, Ziyao and Zheng, Duo and Li, Zhixu and Qu, Jianfeng and Zhou, Jie

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7716--7729

We present ClidSum, a benchmark dataset towards building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents and 112k+ annotated summaries in different target languages. Based on the proposed ClidSum, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively. We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on ClidSum to provide deeper analyses. Furthermore, we propose mDialBART which extends mBART via further pre-training, where the multiple objectives help the pre-trained model capture the structural characteristics as well as key content in dialogues and the transformation from source to the target language. Experimental results show the superiority of mDialBART, as an end-to-end model, outperforms strong pipeline models on ClidSum. Finally, we discuss specific challenges that current approaches faced with this task and give multiple promising directions for future research. We have released the dataset and code at https://github.com/krystalan/ClidSum.

null

10.18653/v1/2022.emnlp-main.526

null

27,656

inproceedings

muller-eberstein-etal-2022-spectral

Spectral Probing

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.527/

M{\"uller-Eberstein, Max and van der Goot, Rob and Plank, Barbara

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7730--7741

Linguistic information is encoded at varying timescales (subwords, phrases, etc.) and communicative levels, such as syntax and semantics. Contextualized embeddings have analogously been found to capture these phenomena at distinctive layers and frequencies. Leveraging these findings, we develop a fully learnable frequency filter to identify spectral profiles for any given task. It enables vastly more granular analyses than prior handcrafted filters, and improves on efficiency. After demonstrating the informativeness of spectral probing over manual filters in a monolingual setting, we investigate its multilingual characteristics across seven diverse NLP tasks in six languages. Our analyses identify distinctive spectral profiles which quantify cross-task similarity in a linguistically intuitive manner, while remaining consistent across languages{---}highlighting their potential as robust, lightweight task descriptors.

null

10.18653/v1/2022.emnlp-main.527

null

27,657

inproceedings

klein-etal-2022-qasem

{QAS}em Parsing: Text-to-text Modeling of {QA}-based Semantics

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.528/

Klein, Ayal and Hirsch, Eran and Eliav, Ron and Pyatkin, Valentina and Caciularu, Avi and Dagan, Ido

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7742--7756

Various works suggest the appeals of incorporating explicit semantic representations when addressing challenging realistic NLP scenarios. Common approaches offer either comprehensive linguistically-based formalisms, like AMR, or alternatively Open-IE, which provides a shallow and partial representation. More recently, an appealing trend introduces semi-structured natural-language structures as an intermediate meaning-capturing representation, often in the form of questions and answers.In this work, we further promote this line of research by considering three prior QA-based semantic representations. These cover verbal, nominalized and discourse-based predications, regarded as jointly providing a comprehensive representation of textual information {---} termed QASem. To facilitate this perspective, we investigate how to best utilize pre-trained sequence-to-sequence language models, which seem particularly promising for generating representations that consist of natural language expressions (questions and answers). In particular, we examine and analyze input and output linearization strategies, as well as data augmentation and multitask learning for a scarce training data setup. Consequently, we release the first unified QASem parsing tool, easily applicable for downstream tasks that can benefit from an explicit semi-structured account of information units in text.

null

10.18653/v1/2022.emnlp-main.528

null

27,658

inproceedings

zhao-etal-2022-keyphrase

Keyphrase Generation via Soft and Hard Semantic Corrections

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.529/

Zhao, Guangzhen and Yin, Guoshun and Yang, Peng and Yao, Yu

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7757--7768

Keyphrase generation aims to generate a set of condensed phrases given a source document. Although maximum likelihood estimation (MLE) based keyphrase generation methods have shown impressive performance, they suffer from the bias on the source-prediction sequence pair and the bias on the prediction-target pair. To tackle the above biases, we propose a novel correction model CorrKG on top of the MLE pipeline, where the biases are corrected via the optimal transport (OT) and a frequency-based filtering-and-sorting (FreqFS) strategy. Specifically, OT is introduced as soft correction to facilitate the alignment of salient information and rectify the semantic bias in the source document and predicted keyphrases pair. An adaptive semantic mass learning scheme is conducted on the vanilla OT to achieve a proper pair-wise optimal transport procedure, which promotes the OT learning brought by rectifying semantic masses dynamically. Besides, the FreqFS strategy is designed as hard correction to reduce the bias of predicted and ground truth keyphrases, and thus to generate accurate and sufficient keyphrases. Extensive experiments over multiple benchmark datasets show that our model achieves superior keyphrase generation as compared with the state-of-the-arts.

null

10.18653/v1/2022.emnlp-main.529

null

27,659

inproceedings

jung-etal-2022-modal

Modal-specific Pseudo Query Generation for Video Corpus Moment Retrieval

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.530/

Jung, Minjoon and Choi, SeongHo and Kim, JooChan and Kim, Jin-Hwa and Zhang, Byoung-Tak

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7769--7781

Video corpus moment retrieval (VCMR) is the task to retrieve the most relevant video moment from a large video corpus using a natural language query.For narrative videos, e.g., drama or movies, the holistic understanding of temporal dynamics and multimodal reasoning are crucial.Previous works have shown promising results; however, they relied on the expensive query annotations for the VCMR, i.e., the corresponding moment intervals.To overcome this problem, we propose a self-supervised learning framework: Modal-specific Pseudo Query Generation Network (MPGN).First, MPGN selects candidate temporal moments via subtitle-based moment sampling.Then, it generates pseudo queries exploiting both visualand textual information from the selected temporal moments.Through the multimodal information in the pseudo queries, we show that MPGN successfully learns to localize the video corpus moment without any explicit annotation.We validate the effectiveness of MPGN on TVR dataset, showing the competitive results compared with both supervised models and unsupervised setting models.

null

10.18653/v1/2022.emnlp-main.530

null

27,660

inproceedings

zhu-etal-2022-duqm

{D}u{QM}: A {C}hinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.531/

Zhu, Hongyu and Chen, Yan and Yan, Jing and Liu, Jing and Hong, Yu and Chen, Ying and Wu, Hua and Wang, Haifeng

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7782--7794

In this paper, we focus on the robustness evaluation of Chinese Question Matching (QM) models. Most of the previous work on analyzing robustness issues focus on just one or a few types of artificial adversarial examples. Instead, we argue that a comprehensive evaluation should be conducted on natural texts, which takes into account the fine-grained linguistic capabilities of QM models. For this purpose, we create a Chinese dataset namely DuQM which contains natural questions with linguistic perturbations to evaluate the robustness of QM models. DuQM contains 3 categories and 13 subcategories with 32 linguistic perturbations. The extensive experiments demonstrate that DuQM has a better ability to distinguish different models. Importantly, the detailed breakdown of evaluation by the linguistic phenomena in DuQM helps us easily diagnose the strength and weakness of different models. Additionally, our experiment results show that the effect of artificial adversarial examples does not work on natural texts. Our baseline codes and a leaderboard are now publicly available.

null

10.18653/v1/2022.emnlp-main.531

null

27,661

inproceedings

sarti-etal-2022-divemt

{D}iv{EMT}: Neural Machine Translation Post-Editing Effort Across Typologically Diverse Languages

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.532/

Sarti, Gabriele and Bisazza, Arianna and Guerberof-Arenas, Ana and Toral, Antonio

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7795--7816

We introduce DivEMT, the first publicly available post-editing study of Neural Machine Translation (NMT) over a typologically diverse set of target languages. Using a strictly controlled setup, 18 professional translators were instructed to translate or post-edit the same set of English documents into Arabic, Dutch, Italian, Turkish, Ukrainian, and Vietnamese. During the process, their edits, keystrokes, editing times and pauses were recorded, enabling an in-depth, cross-lingual evaluation of NMT quality and post-editing effectiveness. Using this new dataset, we assess the impact of two state-of-the-art NMT systems, Google Translate and the multilingual mBART-50 model, on translation productivity. We find that post-editing is consistently faster than translation from scratch. However, the magnitude of productivity gains varies widely across systems and languages, highlighting major disparities in post-editing effectiveness for languages at different degrees of typological relatedness to English, even when controlling for system architecture and training data size. We publicly release the complete dataset including all collected behavioral data, to foster new research on the translation capabilities of NMT systems for typologically diverse languages.

null

10.18653/v1/2022.emnlp-main.532

null

27,662

inproceedings

hessenthaler-etal-2022-bridging

Bridging Fairness and Environmental Sustainability in Natural Language Processing

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.533/

Hessenthaler, Marius and Strubell, Emma and Hovy, Dirk and Lauscher, Anne

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7817--7836

Fairness and environmental impact are important research directions for the sustainable development of artificial intelligence. However, while each topic is an active research area in natural language processing (NLP), there is a surprising lack of research on the interplay between the two fields. This lacuna is highly problematic, since there is increasing evidence that an exclusive focus on fairness can actually hinder environmental sustainability, and vice versa. In this work, we shed light on this crucial intersection in NLP by (1) investigating the efficiency of current fairness approaches through surveying example methods for reducing unfair stereotypical bias from the literature, and (2) evaluating a common technique to reduce energy consumption (and thus environmental impact) of English NLP models, knowledge distillation (KD), for its impact on fairness. In this case study, we evaluate the effect of important KD factors, including layer and dimensionality reduction, with respect to: (a) performance on the distillation task (natural language inference and semantic similarity prediction), and (b) multiple measures and dimensions of stereotypical bias (e.g., gender bias measured via the Word Embedding Association Test). Our results lead us to clarify current assumptions regarding the effect of KD on unfair bias: contrary to other findings, we show that KD can actually decrease model fairness.

null

10.18653/v1/2022.emnlp-main.533

null

27,663

inproceedings

hu-etal-2022-unimse

{U}ni{MSE}: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.534/

Hu, Guimin and Lin, Ting-En and Zhao, Yi and Lu, Guangming and Wu, Yuchuan and Li, Yongbin

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7837--7851

Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are key research topics for computers to understand human behaviors. From a psychological perspective, emotions are the expression of affect or feelings during a short period, while sentiments are formed and held for a longer period. However, most existing works study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two. In this paper, we propose a multimodal sentiment knowledge-sharing framework (UniMSE) that unifies MSA and ERC tasks from features, labels, and models. We perform modality fusion at the syntactic and semantic levels and introduce contrastive learning between modalities and samples to better capture the difference and consistency between sentiments and emotions. Experiments on four public benchmark datasets, MOSI, MOSEI, MELD, and IEMOCAP, demonstrate the effectiveness of the proposed method and achieve consistent improvements compared with state-of-the-art methods.

null

10.18653/v1/2022.emnlp-main.534

null

27,664

inproceedings

zhang-etal-2022-brain

Is the Brain Mechanism for Hierarchical Structure Building Universal Across Languages? An f{MRI} Study of {C}hinese and {E}nglish

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.535/

Zhang, Xiaohan and Wang, Shaonan and Lin, Nan and Zong, Chengqing

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7852--7861

Evidence from psycholinguistic studies suggests that the human brain builds a hierarchical syntactic structure during language comprehension. However, it is still unknown whether the neural basis of such structures is universal across languages. In this paper, we first analyze the differences in language structure between two diverse languages: Chinese and English. By computing the working memory requirements when applying parsing strategies to different language structures, we find that top-down parsing generates less memory load for the right-branching English and bottom-up parsing is less memory-demanding for Chinese.Then we use functional magnetic resonance imaging (fMRI) to investigate whether the brain has different syntactic adaptation strategies in processing Chinese and English. Specifically, for both Chinese and English, we extract predictors from the implementations of different parsing strategies, i.e., bottom-up and top-down. Then, these predictors are separately associated with fMRI signals. Results show that for Chinese and English, the brain utilizes bottom-up and top-down parsing strategies separately. These results reveal that the brain adopts parsing strategies with less memory processing load according to different language structures.

null

10.18653/v1/2022.emnlp-main.535

null

27,665

inproceedings

xue-aletras-2022-hashformers

{H}ash{F}ormers: Towards Vocabulary-independent Pre-trained Transformers

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.536/

Xue, Huiyin and Aletras, Nikolaos

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7862--7874

Transformer-based pre-trained language models are vocabulary-dependent, mapping by default each token to its corresponding embedding. This one-to-one mapping results into embedding matrices that occupy a lot of memory (i.e. millions of parameters) and grow linearly with the size of the vocabulary. Previous work on on-device transformers dynamically generate token embeddings on-the-fly without embedding matrices using locality-sensitive hashing over morphological information. These embeddings are subsequently fed into transformer layers for text classification. However, these methods are not pre-trained. Inspired by this line of work, we propose HashFormers, a new family of vocabulary-independent pre-trained transformers that support an unlimited vocabulary (i.e. all possible tokens in a corpus) given a substantially smaller fixed-sized embedding matrix. We achieve this by first introducing computationally cheap hashing functions that bucket together individual tokens to embeddings. We also propose three variants that do not require an embedding matrix at all, further reducing the memory requirements. We empirically demonstrate that HashFormers are more memory efficient compared to standard pre-trained transformers while achieving comparable predictive performance when fine-tuned on multiple text classification tasks. For example, our most efficient HashFormer variant has a negligible performance degradation (0.4{\%} on GLUE) using only 99.1K parameters for representing the embeddings compared to 12.3-38M parameters of state-of-the-art models.

null

10.18653/v1/2022.emnlp-main.536

null

27,666

inproceedings

wang-etal-2022-matchprompt

{M}atch{P}rompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.537/

Wang, Jiaxin and Zhang, Lingling and Liu, Jun and Liang, Xi and Zhong, Yujie and Wu, Yaqiang

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7875--7888

Relation clustering is a general approach for open relation extraction (OpenRE). Current methods have two major problems. One is that their good performance relies on large amounts of labeled and pre-defined relational instances for pre-training, which are costly to acquire in reality. The other is that they only focus on learning a high-dimensional metric space to measure the similarity of novel relations and ignore the specific relational representations of clusters. In this work, we propose a new prompt-based framework named MatchPrompt, which can realize OpenRE with efficient knowledge transfer from only a few pre-defined relational instances as well as mine the specific meanings for cluster interpretability. To our best knowledge, we are the first to introduce a prompt-based framework for unlabeled clustering. Experimental results on different datasets show that MatchPrompt achieves the new SOTA results for OpenRE.

null

10.18653/v1/2022.emnlp-main.537

null

27,667

inproceedings

hu-etal-2022-improving-aspect

Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.538/

Hu, Mengting and Wu, Yike and Gao, Hang and Bai, Yinhao and Zhao, Shiwan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7889--7900

Recently, aspect sentiment quad prediction (ASQP) has become a popular task in the field of aspect-level sentiment analysis. Previous work utilizes a predefined template to paraphrase the original sentence into a structure target sequence, which can be easily decoded as quadruplets of the form (aspect category, aspect term, opinion term, sentiment polarity). The template involves the four elements in a fixed order. However, we observe that this solution contradicts with the order-free property of the ASQP task, since there is no need to fix the template order as long as the quadruplet is extracted correctly. Inspired by the observation, we study the effects of template orders and find that some orders help the generative model achieve better performance. It is hypothesized that different orders provide various views of the quadruplet. Therefore, we propose a simple but effective method to identify the most proper orders, and further combine multiple proper templates as data augmentation to improve the ASQP task. Specifically, we use the pre-trained language model to select the orders with minimal entropy. By fine-tuning the pre-trained language model with these template orders, our approach improves the performance of quad prediction, and outperforms state-of-the-art methods significantly in low-resource settings.

null

10.18653/v1/2022.emnlp-main.538

null

27,668

inproceedings

lauscher-etal-2022-socioprobe

{S}ocio{P}robe: What, When, and Where Language Models Learn about Sociodemographics

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.539/

Lauscher, Anne and Bianchi, Federico and Bowman, Samuel R. and Hovy, Dirk

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7901--7918

Pre-trained language models (PLMs) have outperformed other NLP models on a wide range of tasks. Opting for a more thorough understanding of their capabilities and inner workings, researchers have established the extend to which they capture lower-level knowledge like grammaticality, and mid-level semantic knowledge like factual understanding. However, there is still little understanding of their knowledge of higher-level aspects of language. In particular, despite the importance of sociodemographic aspects in shaping our language, the questions of whether, where, and how PLMs encode these aspects, e.g., gender or age, is still unexplored. We address this research gap by probing the sociodemographic knowledge of different single-GPU PLMs on multiple English data sets via traditional classifier probing and information-theoretic minimum description length probing. Our results show that PLMs do encode these sociodemographics, and that this knowledge is sometimes spread across the layers of some of the tested PLMs. We further conduct a multilingual analysis and investigate the effect of supplementary training to further explore to what extent, where, and with what amount of pre-training data the knowledge is encoded. Our overall results indicate that sociodemographic knowledge is still a major challenge for NLP. PLMs require large amounts of pre-training data to acquire the knowledge and models that excel in general language understanding do not seem to own more knowledge about these aspects.

null

10.18653/v1/2022.emnlp-main.539

null

27,669

inproceedings

ustun-cooper-stickland-2022-parameter

When does Parameter-Efficient Transfer Learning Work for Machine Translation?

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.540/

{\"Ust{\"un, Ahmet and Cooper Stickland, Asa

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7919--7933

Parameter-efficient fine-tuning methods (PEFTs) offer the promise of adapting large pre-trained models while only tuning a small number of parameters. They have been shown to be competitive with full model fine-tuning for many downstream tasks. However, prior work indicates that PEFTs may not work as well for machine translation (MT), and there is no comprehensive study showing when PEFTs work for MT. We conduct a comprehensive empirical study of PEFTs for MT, considering (1) various parameter budgets, (2) a diverse set of language-pairs, and (3) different pre-trained models. We find that {\textquoteleft}adapters', in which small feed-forward networks are added after every layer, are indeed on par with full model fine-tuning when the parameter budget corresponds to 10{\%} of total model parameters. Nevertheless, as the number of tuned parameters decreases, the performance of PEFTs decreases. The magnitude of this decrease depends on the language pair, with PEFTs particularly struggling for distantly related language-pairs. We find that using PEFTs with a larger pre-trained model outperforms full fine-tuning with a smaller model, and for smaller training data sizes, PEFTs outperform full fine-tuning for the same pre-trained model.

null

10.18653/v1/2022.emnlp-main.540

null

27,670

inproceedings

ustun-etal-2022-hyper

Hyper-{X}: A Unified Hypernetwork for Multi-Task Multilingual Transfer

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.541/

{\"Ust{\"un, Ahmet and Bisazza, Arianna and Bouma, Gosse and van Noord, Gertjan and Ruder, Sebastian

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7934--7949

Massively multilingual models are promising for transfer learning across tasks and languages. However, existing methods are unable to fully leverage training data when it is available in different task-language combinations. To exploit such heterogeneous supervision, we propose Hyper-X, a single hypernetwork that unifies multi-task and multilingual learning with efficient adaptation. It generates weights for adapter modules conditioned on both tasks and language embeddings. By learning to combine task and language-specific knowledge, our model enables zero-shot transfer for unseen languages and task-language combinations. Our experiments on a diverse set of languages demonstrate that Hyper-X achieves the best or competitive gain when a mixture of multiple resources is available, while on par with strong baseline in the standard scenario. Hyper-X is also considerably more efficient in terms of parameters and resources compared to methods that train separate adapters. Finally, Hyper-X consistently produces strong results in few-shot scenarios for new languages, showing the versatility of our approach beyond zero-shot transfer.

null

10.18653/v1/2022.emnlp-main.541

null

27,671

inproceedings

xu-etal-2022-towards-robust

Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of {NLP} Systems

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.542/

Xu, Jialiang and Zhou, Mengyu and He, Xinyi and Han, Shi and Zhang, Dongmei

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7950--7966

Numerical Question Answering is the task of answering questions that require numerical capabilities. Previous works introduce general adversarial attacks to Numerical Question Answering, while not systematically exploring numerical capabilities specific to the topic. In this paper, we propose to conduct numerical capability diagnosis on a series of Numerical Question Answering systems and datasets. A series of numerical capabilities are highlighted, and corresponding dataset perturbations are designed. Empirical results indicate that existing systems are severely challenged by these perturbations. E.g., Graph2Tree experienced a 53.83{\%} absolute accuracy drop against the {\textquotedblleft}Extra{\textquotedblright} perturbation on ASDiv-a, and BART experienced 13.80{\%} accuracy drop against the {\textquotedblleft}Language{\textquotedblright} perturbation on the numerical subset of DROP. As a counteracting approach, we also investigate the effectiveness of applying perturbations as data augmentation to relieve systems' lack of robust numerical capabilities. With experiment analysis and empirical studies, it is demonstrated that Numerical Question Answering with robust numerical capabilities is still to a large extent an open question. We discuss future directions of Numerical Question Answering and summarize guidelines on future dataset collection and system design.

null

10.18653/v1/2022.emnlp-main.542

null

27,672

inproceedings

song-etal-2022-enhancing

Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.543/

Song, Mengxiao and Yu, Bowen and Quangang, Li and Yubin, Wang and Liu, Tingwen and Xu, Hongbo

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7967--7977

Multi-intent detection and slot filling joint model attracts more and more attention since it can handle multi-intent utterances, which is closer to complex real-world scenarios. Most existing joint models rely entirely on the training procedure to obtain the implicit correlation between intents and slots. However, they ignore the fact that leveraging the rich global knowledge in the corpus can determine the intuitive and explicit correlation between intents and slots. In this paper, we aim to make full use of the statistical co-occurrence frequency between intents and slots as prior knowledge to enhance joint multiple intent detection and slot filling. To be specific, an intent-slot co-occurrence graph is constructed based on the entire training corpus to globally discover correlation between intents and slots. Based on the global intent-slot co-occurrence, we propose a novel graph neural network to model the interaction between the two subtasks. Experimental results on two public multi-intent datasets demonstrate that our approach outperforms the state-of-the-art models.

null

10.18653/v1/2022.emnlp-main.543

null

27,673

inproceedings

giulianelli-2022-towards

Towards Pragmatic Production Strategies for Natural Language Generation Tasks

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.544/

Giulianelli, Mario

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7978--7984

This position paper proposes a conceptual framework for the design of Natural Language Generation (NLG) systems that follow efficient and effective production strategies in order to achieve complex communicative goals. In this general framework, efficiency is characterised as the parsimonious regulation of production and comprehension costs while effectiveness is measured with respect to task-oriented and contextually grounded communicative goals. We provide concrete suggestions for the estimation of goals, costs, and utility via modern statistical methods, demonstrating applications of our framework to the classic pragmatic task of visually grounded referential games and to abstractive text summarisation, two popular generation tasks with real-world applications. In sum, we advocate for the development of NLG systems that learn to make pragmatic production decisions from experience, by reasoning about goals, costs, and utility in a human-like way.

null

10.18653/v1/2022.emnlp-main.544

null

27,674

inproceedings

chen-etal-2022-litevl

{L}ite{VL}: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.545/

Chen, Dongsheng and Tao, Chaofan and Hou, Lu and Shang, Lifeng and Jiang, Xin and Liu, Qun

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7985--7997

Recent large-scale video-language pre-trained models have shown appealing performance on various downstream tasks. However, the pre-training process is computationally expensive due to the requirement of millions of video-text pairs and the redundant data structure of each video. To mitigate these problems, we propose LiteVL, which adapts a pre-trained image-language model BLIP into a video-text model directly on downstream tasks, without heavy pre-training. To enhance the temporal modeling lacking in the image-language model, we propose to add temporal attention modules in the image encoder of BLIP with dynamic temporal scaling. Besides the model-wise adaptation, we also propose a non-parametric pooling mechanism to adaptively reweight the fine-grained video embedding conditioned on the text. Experimental results on text-video retrieval and video question answering show that the proposed LiteVL even outperforms previous video-language pre-trained models by a clear margin, though without any video-language pre-training.

null

10.18653/v1/2022.emnlp-main.545

null

27,675

inproceedings

dessi-etal-2022-communication

Communication breakdown: On the low mutual intelligibility between human and neural captioning

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.546/

Dess{\`i}, Roberto and Gualdoni, Eleonora and Franzon, Francesca and Boleda, Gemma and Baroni, Marco

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

7998--8007

We compare the 0-shot performance of a neural caption-based image retriever when given as input either human-produced captions or captions generated by a neural captioner. We conduct this comparison on the recently introduced ImageCoDe data-set (Krojer et al. 2022), which contains hard distractors nearly identical to the images to be retrieved. We find that the neural retriever has much higher performance when fed neural rather than human captions, despite the fact that the former, unlike the latter, were generated without awareness of the distractors that make the task hard. Even more remarkably, when the same neural captions are given to human subjects, their retrieval performance is almost at chance level. Our results thus add to the growing body of evidence that, even when the {\textquotedblleft}language{\textquotedblright} of neural models resembles English, this superficial resemblance might be deeply misleading.

null

10.18653/v1/2022.emnlp-main.546

null

27,676

inproceedings

lee-etal-2022-normalizing

Normalizing Mutual Information for Robust Adaptive Training for Translation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.547/

Lee, Youngwon and Lee, Changmin and Lee, Hojin and Hwang, Seung-won

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8008--8015

Despite the success of neural machine translation models, tensions between fluency of optimizing target language modeling and source-faithfulness remain as challenges. Previously, Conditional Bilingual Mutual Information (CBMI), a scoring metric for the importance of target sentences and tokens, was proposed to encourage fluent and faithful translations. The score is obtained by combining the probability from the translation model and the target language model, which is then used to assign different weights to losses from sentences and tokens. Meanwhile, we argue this metric is not properly normalized, for which we propose Normalized Pointwise Mutual Information (NPMI). NPMI utilizes an additional language model on source language to approximate the joint likelihood of source-target pair and the likelihood of the source, which is then used for normalizing the score. We showed that NPMI better captures the dependence between source-target and that NPMI-based token-level adaptive training brings improvements over baselines with empirical results from En-De, De-En, and En-Ro translation tasks.

null

10.18653/v1/2022.emnlp-main.547

null

27,677

inproceedings

xu-etal-2022-bilingual

Bilingual Synchronization: Restoring Translational Relationships with Editing Operations

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.548/

Xu, Jitao and Crego, Josep and Yvon, Fran{\c{c}}ois

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8016--8030

Machine Translation (MT) is usually viewed as a one-shot process that generates the target language equivalent of some source text from scratch. We consider here a more general setting which assumes an initial target sequence, that must be transformed into a valid translation of the source, thereby restoring parallelism between source and target. For this bilingual synchronization task, we consider several architectures (both autoregressive and non-autoregressive) and training regimes, and experiment with multiple practical settings such as simulated interactive MT, translating with Translation Memory (TM) and TM cleaning. Our results suggest that one single generic edit-based system, once fine-tuned, can compare with, or even outperform, dedicated systems specifically trained for these tasks.

null

10.18653/v1/2022.emnlp-main.548

null

27,678

inproceedings

bonaldi-etal-2022-human

Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.549/

Bonaldi, Helena and Dellantonio, Sara and Tekiro{\u{g}}lu, Serra Sinem and Guerini, Marco

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8031--8049

Fighting online hate speech is a challenge that is usually addressed using Natural Language Processing via automatic detection and removal of hate content. Besides this approach, counter narratives have emerged as an effective tool employed by NGOs to respond to online hate on social media platforms. For this reason, Natural Language Generation is currently being studied as a way to automatize counter narrative writing. However, the existing resources necessary to train NLG models are limited to 2-turn interactions (a hate speech and a counter narrative as response), while in real life, interactions can consist of multiple turns. In this paper, we present a hybrid approach for dialogical data collection, which combines the intervention of human expert annotators over machine generated dialogues obtained using 19 different configurations. The result of this work is DIALOCONAN, the first dataset comprising over 3000 fictitious multi-turn dialogues between a hater and an NGO operator, covering 6 targets of hate.

null

10.18653/v1/2022.emnlp-main.549

null

27,679

inproceedings

liang-etal-2022-janus

{JANUS}: Joint Autoregressive and Non-autoregressive Training with Auxiliary Loss for Sequence Generation

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.550/

Liang, Xiaobo and Wu, Lijun and Li, Juntao and Zhang, Min

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8050--8060

Transformer-based autoregressive and non-autoregressive models have played an essential role in sequence generation tasks. The autoregressive model can obtain excellent performance, while the non-autoregressive model brings fast decoding speed for inference. In this paper, we propose \textbf{JANUS}, a \textbf{J}oint \textbf{A}utoregressive and \textbf{N}on-autoregressive training method using a\textbf{U}xiliary los\textbf{S} to enhance the model performance in both AR and NAR manner simultaneously and effectively alleviate the problem of distribution discrepancy.Further, we pre-train BART with JANUS on a large corpus with minimal cost (16 GPU days) and make the BART-JANUS capable of non-autoregressive generation, demonstrating that our approach can transfer the AR knowledge to NAR. Empirically, we show our approach and BART-JANUS can achieve significant improvement on multiple generation tasks, including machine translation and GLGE benchmarks. Our code is available at Github.

null

10.18653/v1/2022.emnlp-main.550

null

27,680

inproceedings

wu-mooney-2022-entity

Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.551/

Wu, Jialin and Mooney, Raymond

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8061--8072

Most Outside-Knowledge Visual Question Answering (OK-VQA) systems employ a two-stage framework that first retrieves external knowledge given the visual question and then predicts the answer based on the retrieved content. However, the retrieved knowledge is often inadequate. Retrievals are frequently too general and fail to cover specific knowledge needed to answer the question. Also, the naturally available supervision (whether the passage contains the correct answer) is weak and does not guarantee question relevancy. To address these issues, we propose an Entity-Focused Retrieval (EnFoRe) model that provides stronger supervision during training and recognizes question-relevant entities to help retrieve more specific knowledge. Experiments show that our EnFoRe model achieves superior retrieval performance on OK-VQA, the currently largest outside-knowledge VQA dataset. We also combine the retrieved knowledge with state-of-the-art VQA models, and achieve a new state-of-the-art performance on OK-VQA.

null

10.18653/v1/2022.emnlp-main.551

null

27,681

inproceedings

xu-etal-2022-cross

Cross-Linguistic Syntactic Difference in Multilingual {BERT}: How Good is It and How Does It Affect Transfer?

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.552/

Xu, Ningyu and Gui, Tao and Ma, Ruotian and Zhang, Qi and Ye, Jingting and Zhang, Menghan and Huang, Xuanjing

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8073--8092

Multilingual BERT (mBERT) has demonstrated considerable cross-lingual syntactic ability, whereby it enables effective zero-shot cross-lingual transfer of syntactic knowledge. The transfer is more successful between some languages, but it is not well understood what leads to this variation and whether it fairly reflects difference between languages. In this work, we investigate the distributions of grammatical relations induced from mBERT in the context of 24 typologically different languages. We demonstrate that the distance between the distributions of different languages is highly consistent with the syntactic difference in terms of linguistic formalisms. Such difference learnt via self-supervision plays a crucial role in the zero-shot transfer performance and can be predicted by variation in morphosyntactic properties between languages. These results suggest that mBERT properly encodes languages in a way consistent with linguistic diversity and provide insights into the mechanism of cross-lingual transfer.

null

10.18653/v1/2022.emnlp-main.552

null

27,682

inproceedings

bianchi-etal-2022-just

{\textquotedblleft}It`s Not Just Hate{\textquotedblright}: A Multi-Dimensional Perspective on Detecting Harmful Speech Online

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.553/

Bianchi, Federico and HIlls, Stefanie and Rossini, Patricia and Hovy, Dirk and Tromble, Rebekah and Tintarev, Nava

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8093--8099

Well-annotated data is a prerequisite for good Natural Language Processing models. Too often, though, annotation decisions are governed by optimizing time or annotator agreement. We make a case for nuanced efforts in an interdisciplinary setting for annotating offensive online speech. Detecting offensive content is rapidly becoming one of the most important real-world NLP tasks. However, most datasets use a single binary label, e.g., for hate or incivility, even though each concept is multi-faceted. This modeling choice severely limits nuanced insights, but also performance.We show that a more fine-grained multi-label approach to predicting incivility and hateful or intolerant content addresses both conceptual and performance issues.We release a novel dataset of over 40,000 tweets about immigration from the US and UK, annotated with six labels for different aspects of incivility and intolerance.Our dataset not only allows for a more nuanced understanding of harmful speech online, models trained on it also outperform or match performance on benchmark datasets

null

10.18653/v1/2022.emnlp-main.553

null

27,683

inproceedings

yang-etal-2022-long

Long Text Generation with Topic-aware Discrete Latent Variable Model

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.554/

Yang, Erguang and Liu, Mingtong and Xiong, Deyi and Zhang, Yujie and Chen, Yufeng and Xu, Jinan

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8100--8107

Generating coherent long texts is an important yet challenging task, particularly forthe open-ended generation. Prior work based on discrete latent codes focuses on the modeling of discourse relation, resulting in discrete codes only learning shallow semantics (Ji and Huang, 2021). A natural text always revolves around several related topics and the transition across them is natural and smooth.In this work, we investigate whether discrete latent codes can learn information of topics. To this end, we build a topic-aware latent code-guided text generation model. To encourage discrete codes to model information about topics, we propose a span-level bag-of-words training objective for the model. Automatic and manual evaluation experiments show that our method can generate more topic-relevant and coherent texts.

null

10.18653/v1/2022.emnlp-main.554

null

27,684

inproceedings

shu-etal-2022-tiara

{TIARA}: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Base

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.555/

Shu, Yiheng and Yu, Zhiwei and Li, Yuhan and Karlsson, B{\"orje and Ma, Tingting and Qu, Yuzhong and Lin, Chin-Yew

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8108--8121

Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios. However, KBQA remains challenging, especially regarding coverage and generalization settings. This is due to two main factors: i) understanding the semantics of both questions and relevant knowledge from the KB; ii) generating executable logical forms with both semantic and syntactic correctness. In this paper, we present a new KBQA model, TIARA, which addresses those issues by applying multi-grained retrieval to help the PLM focus on the most relevant KB context, viz., entities, exemplary logical forms, and schema items. Moreover, constrained decoding is used to control the output space and reduce generation errors. Experiments over important benchmarks demonstrate the effectiveness of our approach. TIARA outperforms previous SOTA, including those using PLMs or oracle entity annotations, by at least 4.1 and 1.1 F1 points on GrailQA and WebQuestionsSP, respectively. Specifically on GrailQA, TIARA outperforms previous models in all categories, with an improvement of 4.7 F1 points in zero-shot generalization.

null

10.18653/v1/2022.emnlp-main.555

null

27,685

inproceedings

wang-etal-2022-structure

Structure-Unified {M}-Tree Coding Solver for Math Word Problem

Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue

dec

2022

Abu Dhabi, United Arab Emirates

Association for Computational Linguistics

https://aclanthology.org/2022.emnlp-main.556/

Wang, Bin and Ju, Jiangzhou and Fan, Yang and Dai, Xinyu and Huang, Shujian and Chen, Jiajun

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

8122--8132

As one of the challenging NLP tasks, designing math word problem (MWP) solvers has attracted increasing research attention for the past few years. In previous work, models designed by taking into account the properties of the binary tree structure of mathematical expressions at the output side have achieved better performance. However, the expressions corresponding to a MWP are often diverse (e.g., $n_1+n_2 \times n_3-n_4$, $n_3\times n_2-n_4+n_1$, etc.), and so are the corresponding binary trees, which creates difficulties in model learning due to the non-deterministic output space. In this paper, we propose the Structure-Unified M-Tree Coding Solver (SUMC-Solver), which applies a tree with any M branches (M-tree) to unify the output structures. To learn the M-tree, we use a mapping to convert the M-tree into the M-tree codes, where codes store the information of the paths from tree root to leaf nodes and the information of leaf nodes themselves, and then devise a Sequence-to-Code (seq2code) model to generate the codes. Experimental results on the widely used MAWPS and Math23K datasets have demonstrated that SUMC-Solver not only outperforms several state-of-the-art models under similar experimental settings but also performs much better under low-resource conditions.

null

10.18653/v1/2022.emnlp-main.556

null

27,686