mtybilly/Instruct2DS huggingface dataset:
FinQA: A Dataset of Numerical Reasoning over Financial Data
Authors: Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan Routledge, William Yang Wang Zhiyu Chen
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: The sheer volume of financial statements makes it difficult for humans to access and analyze a business`s financials. Robust numerical reasoning likewise faces unique challenges in this domain. In this work, we focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations. To facilitate analytical progress, we propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. We also annotate the gold reasoning programs to ensure full explainability. We further introduce baselines and conduct comprehensive experiments in our dataset. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge and in complex multi-step numerical reasoning on that knowledge. Our dataset -- the first of its kind -- should therefore enable significant, new community research into complex application domains. The dataset and code are publicly available at r̆lhttps://github.com/czyssrs/FinQA.
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation
Authors: Bhargavi Paranjape, Asish Ghoshal, Scott Yih, Yashar Mehdad, Srini Iyer Kushal Lakhotia
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Natural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for tasks such as Question Answering (QA) and Fact Verification. Recently, pre-trained sequence to sequence (seq2seq) models have proven to be very effective in jointly making predictions, as well as generating NL explanations. However, these models have many shortcomings; they can fabricate explanations even for incorrect predictions, they are difficult to adapt to long input documents, and their training requires a large amount of labeled data. In this paper, we develop FiD-Ex, which addresses these shortcomings for seq2seq models by: 1) introducing sentence markers to eliminate explanation fabrication by encouraging extractive generation, 2) using the fusion-in-decoder architecture to handle long input contexts, and 3) intermediate fine-tuning on re-structured open domain QA datasets to improve few-shot performance. FiD-Ex significantly improves over prior work in terms of explanation metrics and task accuracy on five tasks from the ERASER explainability benchmark in both fully supervised and few-shot settings.
RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models
Authors: Wenyang Gao, Jun Yan, Ryan Moreno, Xiang Ren Bill Yuchen Lin
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: To audit the robustness of named entity recognition (NER) models, we propose RockNER, a simple yet effective method to create natural adversarial examples. Specifically, at the entity level, we replace target entities with other entities of the same semantic class in Wikidata; at the context level, we use pre-trained language models (e.g., BERT) to generate word substitutions. Together, the two levels of at- tack produce natural adversarial examples that result in a shifted distribution from the training data on which our target models have been trained. We apply the proposed method to the OntoNotes dataset and create a new benchmark named OntoRock for evaluating the robustness of existing NER models via a systematic evaluation protocol. Our experiments and analysis reveal that even the best model has a significant performance drop, and these models seem to memorize in-domain entity patterns instead of reasoning from the context. Our work also studies the effects of a few simple data augmentation methods to improve the robustness of NER models.
Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI
Authors: Yitian Li, Wenqing Chen, Liqiang Xiao, Hao He, Yaohui Jin Jidong Tian
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Recently, language models (LMs) have achieved significant performance on many NLU tasks, which has spurred widespread interest for their possible applications in the scientific and social area. However, LMs have faced much criticism of whether they are truly capable of reasoning in NLU. In this work, we propose a diagnostic method for first-order logic (FOL) reasoning with a new proposed benchmark, LogicNLI. LogicNLI is an NLI-style dataset that effectively disentangles the target FOL reasoning from commonsense inference and can be used to diagnose LMs from four perspectives: accuracy, robustness, generalization, and interpretability. Experiments on BERT, RoBERTa, and XLNet, have uncovered the weaknesses of these LMs on FOL reasoning, which motivates future exploration to enhance the reasoning ability.
Constructing a Psychometric Testbed for Fair Natural Language Processing
Authors: David Dobolyi, John P. Lalor, Richard G. Netemeyer, Kendall Smith, Yi Yang Ahmed Abbasi
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Psychometric measures of ability, attitudes, perceptions, and beliefs are crucial for understanding user behavior in various contexts including health, security, e-commerce, and finance. Traditionally, psychometric dimensions have been measured and collected using survey-based methods. Inferring such constructs from user-generated text could allow timely, unobtrusive collection and analysis. In this paper we describe our efforts to construct a corpus for psychometric natural language processing (NLP) related to important dimensions such as trust, anxiety, numeracy, and literacy, in the health domain. We discuss our multi-step process to align user text with their survey-based response items and provide an overview of the resulting testbed which encompasses survey-based psychometric measures and accompanying user-generated text from 8,502 respondents. Our testbed also encompasses self-reported demographic information, including race, sex, age, income, and education - thereby affording opportunities for measuring bias and benchmarking fairness of text classification methods. We report preliminary results on use of the text to predict/categorize users' survey response labels - and on the fairness of these models. We also discuss the important implications of our work and resulting testbed for future NLP research on psychometrics and fairness.
COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval
Authors: Heming Sun, Xiang Yue, Simon Lin, Huan Sun Xinliang Frederick Zhang
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval. Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, Query Bank and Relevance Set. The FAQ Bank contains ~16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO). For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1,236 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query. We analyze COUGH by testing different FAQ retrieval models built on top of BM25 and BERT, among which the best model achieves 48.8 under P@5, indicating a great challenge presented by COUGH and encouraging future research for further improvement. Our COUGH dataset is available at r̆lhttps://github.com/sunlab-osu/covid-faq.
Chinese WPLC: A Chinese Dataset for Evaluating Pretrained Language Models on Word Prediction Given Long-Range Context
Authors: Chenxi Sun, Deyi Xiong, Qun Liu Huibin Ge
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: This paper presents a Chinese dataset for evaluating pretrained language models on Word Prediction given Long-term Context (Chinese WPLC). We propose both automatic and manual selection strategies tailored to Chinese to guarantee that target words in passages collected from over 69K novels can only be predicted with long-term context beyond the scope of sentences containing the target words. Dataset analysis reveals that the types of target words range from common nouns to Chinese 4-character idioms. We also observe that linguistic relations between target words and long-range context exhibit diversity, including lexical match, synonym, summary and reasoning. Experiment results show that the Chinese pretrained language model PanGu-$α$ is 45 points behind human in terms of top-1 word prediction accuracy, indicating that Chinese WPLC is a challenging dataset. The dataset is publicly available at r̆lhttps://git.openi.org.cn/PCL-Platform.Intelligence/Chinese_WPLC.
WinoLogic: A Zero-Shot Logic-based Diagnostic Dataset for Winograd Schema Challenge
Authors: Canming Huang, Yongmei Liu, Xiaodan Zhu Weinan He
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: The recent success of neural language models (NLMs) on the Winograd Schema Challenge has called for further investigation of the commonsense reasoning ability of these models. Previous diagnostic datasets rely on crowd-sourcing which fails to provide coherent commonsense crucial for solving WSC problems. To better evaluate NLMs, we propose a logic-based framework that focuses on high-quality commonsense knowledge. Specifically, we identify and collect formal knowledge formulas verified by theorem provers and translate such formulas into natural language sentences. Based on these true knowledge sentences, adversarial false ones are generated. We propose a new dataset named WinoLogic with these sentences. Given a problem in WinoLogic, NLMs need to decide whether the plausible knowledge sentences could correctly solve the corresponding WSC problems in a zero-shot setting. We also ask human annotators to validate WinoLogic to ensure it is human-agreeable. Experiments show that NLMs still struggle to comprehend commonsense knowledge as humans do, indicating that their reasoning ability could have been overestimated.
Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution
Authors: Shun Kiyono, Yuichiroh Matsubayashi, Hiroki Ouchi, Kentaro Inui Ryuto Konno
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Masked language models (MLMs) have contributed to drastic performance improvements with regard to zero anaphora resolution (ZAR). To further improve this approach, in this study, we made two proposals. The first is a new pretraining task that trains MLMs on anaphoric relations with explicit supervision, and the second proposal is a new finetuning method that remedies a notorious issue, the pretrain-finetune discrepancy. Our experiments on Japanese ZAR demonstrated that our two proposals boost the state-of-the-art performance, and our detailed analysis provides new insights on the remaining challenges.
Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast
Authors: Wei Zhao, Jingming Liu Liang Wang
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: In this paper, we propose to align sentence representations from different languages into a unified embedding space, where semantic similarities (both cross-lingual and monolingual) can be computed with a simple dot product. Pre-trained language models are fine-tuned with the translation ranking task. Existing work (Feng et al., 2020) uses sentences within the same batch as negatives, which can suffer from the issue of easy negatives. We adapt MoCo (He et al., 2020) to further improve the quality of alignment. As the experimental results show, the sentence representations produced by our model achieve the new state-of-the-art on several tasks, including Tatoeba en-zh similarity search (Artetxe andSchwenk, 2019b), BUCC en-zh bitext mining, and semantic textual similarity on 7 datasets.
Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers
Authors: Lizhen Qu, Gholamreza Haffari Zhuang Li
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: This paper investigates continual learning for semantic parsing. In this setting, a neural semantic parser learns tasks sequentially without accessing full training data from previous tasks. Direct application of the SOTA continual learning algorithms to this problem fails to achieve comparable performance with re-training models with all seen tasks because they have not considered the special properties of structured outputs yielded by semantic parsers. Therefore, we propose TotalRecall, a continual learning method designed for neural semantic parsers from two aspects: i) a sampling method for memory replay that diversifies logical form templates and balances distributions of parse actions in a memory; ii) a two-stage training method that significantly improves generalization capability of the parsers across tasks. We conduct extensive experiments to study the research problems involved in continual semantic parsing and demonstrate that a neural semantic parser trained with TotalRecall achieves superior performance than the one trained directly with the SOTA continual learning algorithms and achieve a 3-6 times speedup compared to re-training from scratch.
Exophoric Pronoun Resolution in Dialogues with Topic Regularization
Authors: Hongming Zhang, Yangqiu Song, Changshui Zhang, Kun Xu, Dong Yu Xintong Yu
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Resolving pronouns to their referents has long been studied as a fundamental natural language understanding problem. Previous works on pronoun coreference resolution (PCR) mostly focus on resolving pronouns to mentions in text while ignoring the exophoric scenario. Exophoric pronouns are common in daily communications, where speakers may directly use pronouns to refer to some objects present in the environment without introducing the objects first. Although such objects are not mentioned in the dialogue text, they can often be disambiguated by the general topics of the dialogue. Motivated by this, we propose to jointly leverage the local context and global topics of dialogues to solve the out-of-text PCR problem. Extensive experiments demonstrate the effectiveness of adding topic regularization for resolving exophoric pronouns.
Context-Aware Interaction Network for Question Matching
Authors: Zuohui Fu, Yu Yin, Gerard de Melo Zhe Hu
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentence representations. However, regular cross-attention focuses on word-level links between the two input sequences, neglecting the importance of contextual information. We propose a context-aware interaction network (COIN) to properly align two sequences and infer their semantic relationship. Specifically, each interaction block includes (1) a context-aware cross-attention mechanism to effectively integrate contextual information when aligning two sequences, and (2) a gate fusion layer to flexibly interpolate aligned representations. We apply multiple stacked interaction blocks to produce alignments at different levels and gradually refine the attention results. Experiments on two question matching datasets and detailed analyses demonstrate the effectiveness of our model.
TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths
Authors: Hongyuan Xu, Yanlong Wen, Ning Jiang, HaiYing Wu, Xiaojie Yuan Zichen Liu
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: As an essential form of knowledge representation, taxonomies are widely used in various downstream natural language processing tasks. However, with the continuously rising of new concepts, many existing taxonomies are unable to maintain coverage by manual expansion. In this paper, we propose TEMP, a self-supervised taxonomy expansion method, which predicts the position of new concepts by ranking the generated taxonomy-paths. For the first time, TEMP employs pre-trained contextual encoders in taxonomy construction and hypernym detection problems. Experiments prove that pre-trained contextual embeddings are able to capture hypernym-hyponym relations. To learn more detailed differences between taxonomy-paths, we train the model with dynamic margin loss by a novel dynamic margin function. Extensive evaluations exhibit that TEMP outperforms prior state-of-the-art taxonomy expansion approaches by 14.3% in accuracy and 15.8% in mean reciprocal rank on three public benchmarks.
A Graph-Based Neural Model for End-to-End Frame Semantic Parsing
Authors: Yueheng Sun, Meishan Zhang ZhiChao Lin
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently. The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labeling. The three subtasks are closely related while previous studies model them individually, which ignores their intern connections and meanwhile induces error propagation problem. In this work, we propose an end-to-end neural model to tackle the task jointly. Concretely, we exploit a graph-based method, regarding frame semantic parsing as a graph construction problem. All predicates and roles are treated as graph nodes, and their relations are taken as graph edges. Experiment results on two benchmark datasets of frame semantic parsing show that our method is highly competitive, resulting in better performance than pipeline models.
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models
Authors: Wayne Xin Zhao, Sirui Wang, Fuzheng Zhang, Wei Wu, Ji-Rong Wen Kun Zhou
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Recent works have shown that powerful pre-trained language models (PLM) can be fooled by small perturbations or intentional attacks. To solve this issue, various data augmentation techniques are proposed to improve the robustness of PLMs. However, it is still challenging to augment semantically relevant examples with sufficient diversity. In this work, we present Virtual Data Augmentation (VDA), a general framework for robustly fine-tuning PLMs. Based on the original token embeddings, we construct a multinomial mixture for augmenting virtual data embeddings, where a masked language model guarantees the semantic relevance and the Gaussian noise provides the augmentation diversity. Furthermore, a regularized training strategy is proposed to balance the two aspects. Extensive experiments on six datasets show that our approach is able to improve the robustness of PLMs and alleviate the performance degradation under adversarial attacks. Our codes and data are publicly available at bluer̆lhttps://github.com/RUCAIBox/VDA.
CATE: A Contrastive Pre-trained Model for Metaphor Detection with Semi-supervised Learning
Authors: Qianli Ma, Jiangyue Yan, Jieyu Chen Zhenxi Lin
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Metaphors are ubiquitous in natural language, and detecting them requires contextual reasoning about whether a semantic incongruence actually exists. Most existing work addresses this problem using pre-trained contextualized models. Despite their success, these models require a large amount of labeled data and are not linguistically-based. In this paper, we proposed a ContrAstive pre-Trained modEl (CATE) for metaphor detection with semi-supervised learning. Our model first uses a pre-trained model to obtain a contextual representation of target words and employs a contrastive objective to promote an increased distance between target words' literal and metaphorical senses based on linguistic theories. Furthermore, we propose a simple strategy to collect large-scale candidate instances from the general corpus and generalize the model via self-training. Extensive experiments show that CATE achieves better performance against state-of-the-art baselines on several benchmark datasets.
To be Closer: Learning to Link up Aspects with Opinions
Authors: Lejian Liao, Yang Gao, Zhanming Jie, Wei Lu Yuxiang Zhou
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA) (CITATION). However, the trees obtained from off-the-shelf dependency parsers are static, and could be sub-optimal in ABSA. This is because the syntactic trees are not designed for capturing the interactions between opinion words and aspect words. In this work, we aim to shorten the distance between aspects and corresponding opinion words by learning an aspect-centric tree structure. The aspect and opinion words are expected to be closer along such tree structure compared to the standard dependency parse tree. The learning process allows the tree structure to adaptively correlate the aspect and opinion words, enabling us to better identify the polarity in the ABSA task. We conduct experiments on five aspect-based sentiment datasets, and the proposed model significantly outperforms recent strong baselines. Furthermore, our thorough analysis demonstrates the average distance between aspect and opinion words are shortened by at least 19% on the standard SemEval Restaurant14 (CITATION) dataset.
Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model
Authors: Zuchao Li, Hai Zhao, Shu Jiang Hongjiang Jing
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Aspect-based sentiment analysis (ABSA) task consists of three typical subtasks: aspect term extraction, opinion term extraction, and sentiment polarity classification. These three subtasks are usually performed jointly to save resources and reduce the error propagation in the pipeline. However, most of the existing joint models only focus on the benefits of encoder sharing between subtasks but ignore the difference. Therefore, we propose a joint ABSA model, which not only enjoys the benefits of encoder sharing but also focuses on the difference to improve the effectiveness of the model. In detail, we introduce a dual-encoder design, in which a pair encoder especially focuses on candidate aspect-opinion pair classification, and the original encoder keeps attention on sequence labeling. Empirical results show that our proposed model shows robustness and significantly outperforms the previous state-of-the-art on four benchmark datasets.
Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph
Authors: Bin Liang, Jingyi Sun, Yice Zhang, Min Yang, Ruifeng Xu Jianzhu Bao
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Argument pair extraction (APE) aims to extract interactive argument pairs from two passages of a discussion. Previous work studied this task in the context of peer review and rebuttal, and decomposed it into a sequence labeling task and a sentence relation classification task. However, despite the promising performance, such an approach obtains the argument pairs implicitly by the two decomposed tasks, lacking explicitly modeling of the argument-level interactions between argument pairs. In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage. In this manner, two passages can mutually guide each other in the process of APE. Furthermore, we propose an inter-sentence relation graph to effectively model the inter-relations between two sentences and thus facilitates the extraction of argument pairs. Our proposed method can better represent the holistic argument-level semantics and thus explicitly capture the complex correlations between argument pairs. Experimental results show that our approach significantly outperforms the current state-of-the-art model.
Emotion Inference in Multi-Turn Conversations with Addressee-Aware Module and Ensemble Strategy
Authors: Xiaodan Zhu, Yang Li, Suge Wang, Deyu Li, Jian Liao, Jianxing Zheng Dayu Li
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Emotion inference in multi-turn conversations aims to predict the participant`s emotion in the next upcoming turn without knowing the participant`s response yet, and is a necessary step for applications such as dialogue planning. However, it is a severe challenge to perceive and reason about the future feelings of participants, due to the lack of utterance information from the future. Moreover, it is crucial for emotion inference to capture the characteristics of emotional propagation in conversations, such as persistence and contagiousness. In this study, we focus on investigating the task of emotion inference in multi-turn conversations by modeling the propagation of emotional states among participants in the conversation history, and propose an addressee-aware module to automatically learn whether the participant keeps the historical emotional state or is affected by others in the next upcoming turn. In addition, we propose an ensemble strategy to further enhance the model performance. Empirical studies on three different benchmark conversation datasets demonstrate the effectiveness of the proposed model over several strong baselines.
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories
Authors: Guimin Chen, Yuanhe Tian, Yan Song Han Qin
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in a sentence, which is an important task in real-world applications. To perform ABSA, the trained model is required to have a good understanding of the contextual information, especially the particular patterns that suggest the sentiment polarity. However, these patterns typically vary in different sentences, especially when the sentences come from different sources (domains), which makes ABSA still very challenging. Although combining labeled data across different sources (domains) is a promising solution to address the challenge, in practical applications, these labeled data are usually stored at different locations and might be inaccessible to each other due to privacy or legal concerns (e.g., the data are owned by different companies). To address this issue and make the best use of all labeled data, we propose a novel ABSA model with federated learning (FL) adopted to overcome the data isolation limitations and incorporate topic memory (TM) proposed to take the cases of data from diverse sources (domains) into consideration. Particularly, TM aims to identify different isolated data sources due to data inaccessibility by providing useful categorical information for localized predictions. Experimental results on a simulated environment for FL with three nodes demonstrate the effectiveness of our approach, where TM-FL outperforms different baselines including some well-designed FL frameworks.
Comparative Opinion Quintuple Extraction from Product Reviews
Authors: Rui Xia, Jianfei Yu Ziheng Liu
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: As an important task in opinion mining, comparative opinion mining aims to identify comparative sentences from product reviews, extract the comparative elements, and obtain the corresponding comparative opinion tuples. However, most previous studies simply regarded comparative tuple extraction as comparative element extraction, but ignored the fact that many comparative sentences may contain multiple comparisons. The comparative opinion tuples defined in these studies also failed to explicitly provide comparative preferences. To address these limitations, in this work we first introduce a new Comparative Opinion Quintuple Extraction (COQE) task, to identify comparative sentences from product reviews and extract all comparative opinion quintuples (Subject, Object, Comparative Aspect, Comparative Opinion, Comparative Preference). Secondly, based on the existing comparative opinion mining corpora, we make supplementary annotations and construct three datasets for the COQE task. Finally, we benchmark the COQE task by proposing a new BERT-based multi-stage approach as well as three baseline systems extended from previous methods. %The new approach significantly outperforms three baseline systems on three datasets and represents a strong benchmark for COQE. Experimental results show that the new approach significantly outperforms three baseline systems on three datasets for the COQE task.
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations
Authors: Wenbiao Ding, Yu Kang, Tianqiao Liu, Zhongqin Wu, Zitao Liu Hang Li
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms. However, these models are facing challenges of overfitting with limited labels and low model generalization abilities. In this paper, we present a Cross-modal Transformer for Audio-and-Language, i.e., CTAL, which aims to learn the intra-modality and inter-modality connections between audio and language through two proxy tasks on a large amount of audio-and-language pairs: masked language modeling and masked cross-modal acoustic modeling. After fine-tuning our pre-trained model on multiple downstream audio-and-language tasks, we observe significant improvements across various tasks, such as, emotion classification, sentiment analysis, and speaker verification. On this basis, we further propose a specially-designed fusion mechanism that can be used in fine-tuning phase, which allows our pre-trained model to achieve better performance. Lastly, we demonstrate detailed ablation studies to prove that both our novel cross-modality fusion component and audio-language pre-training methods significantly contribute to the promising results. The code and pre-trained models are available at r̆lhttps://github.com/tal-ai/CTAL_EMNLP2021.
Relation-aware Video Reading Comprehension for Temporal Language Grounding
Authors: Xin Sun, Mengmeng Xu, Xi Zhou, Bernard Ghanem Jialin Gao
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Temporal language grounding in videos aims to localize the temporal span relevant to the given query sentence. Previous methods treat it either as a boundary regression task or a span extraction task. This paper will formulate temporal language grounding into video reading comprehension and propose a Relation-aware Network (RaNet) to address it. This framework aims to select a video moment choice from the predefined answer set with the aid of coarse-and-fine choice-query interaction and choice-choice relation construction. A choice-query interactor is proposed to match the visual and textual information simultaneously in sentence-moment and token-moment levels, leading to a coarse-and-fine cross-modal interaction. Moreover, a novel multi-choice relation constructor is introduced by leveraging graph convolution to capture the dependencies among video moment choices for the best choice selection. Extensive experiments on ActivityNet-Captions, TACoS, and Charades-STA demonstrate the effectiveness of our solution. Codes will be available at r̆lhttps://github.com/Huntersxsx/RaNet.
Mutual-Learning Improves End-to-End Speech Translation
Authors: Wei Luo, Boxing Chen, Andrew Gilman Jiawei Zhao
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: A currently popular research area in end-to-end speech translation is the use of knowledge distillation from a machine translation (MT) task to improve the speech translation (ST) task. However, such scenario obviously only allows one way transfer, which is limited by the performance of the teacher model. Therefore, We hypothesis that the knowledge distillation-based approaches are sub-optimal. In this paper, we propose an alternative--a trainable mutual-learning scenario, where the MT and the ST models are collaboratively trained and are considered as peers, rather than teacher/student. This allows us to improve the performance of end-to-end ST more effectively than with a teacher-student paradigm. As a side benefit, performance of the MT model also improves. Experimental results show that in our mutual-learning scenario, models can effectively utilise the auxiliary information from peer models and achieve compelling results on Must-C dataset.
Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization
Authors: Wenliang Dai, Zihan Liu, Pascale Fung Tiezheng Yu
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Multimodal abstractive summarization (MAS) models that summarize videos (vision modality) and their corresponding transcripts (text modality) are able to extract the essential information from massive multimodal data on the Internet. Recently, large-scale generative pre-trained language models (GPLMs) have been shown to be effective in text generation tasks. However, existing MAS models cannot leverage GPLMs' powerful generation ability. To fill this research gap, we aim to study two research questions: 1) how to inject visual information into GPLMs without hurting their generation ability; and 2) where is the optimal place in GPLMs to inject the visual information? In this paper, we present a simple yet effective method to construct vision guided (VG) GPLMs for the MAS task using attention-based add-on layers to incorporate visual information while maintaining their original text generation ability. Results show that our best model significantly surpasses the prior state-of-the-art model by 5.7 ROUGE-1, 5.3 ROUGE-2, and 5.1 ROUGE-L scores on the How2 dataset, and our vision guidance method contributes 83.6% of the overall improvement. Furthermore, we conduct thorough ablation studies to analyze the effectiveness of various modality fusion methods and fusion locations.
Natural Language Video Localization with Learnable Moment Proposals
Authors: Long Chen, Jian Shao, Yueting Zhuang, Jun Xiao Shaoning Xiao
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Given an untrimmed video and a natural language query, Natural Language Video Localization (NLVL) aims to identify the video moment described by query. To address this task, existing methods can be roughly grouped into two groups: 1) propose-and-rank models first define a set of hand-designed moment candidates and then find out the best-matching one. 2) proposal-free models directly predict two temporal boundaries of the referential moment from frames. Currently, almost all the propose-and-rank methods have inferior performance than proposal-free counterparts. In this paper, we argue that the performance of propose-and-rank models are underestimated due to the predefined manners: 1) Hand-designed rules are hard to guarantee the complete coverage of targeted segments. 2) Densely sampled candidate moments cause redundant computation and degrade the performance of ranking process. To this end, we propose a novel model termed LPNet (Learnable Proposal Network for NLVL) with a fixed set of learnable moment proposals. The position and length of these proposals are dynamically adjusted during training process. Moreover, a boundary-aware loss has been proposed to leverage frame-level information and further improve performance. Extensive ablations on two challenging NLVL benchmarks have demonstrated the effectiveness of LPNet over existing state-of-the-art methods.
Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous Environments
Authors: Saim Wani, Shivansh Patel, Unnat Jain, Angel Chang Sonia Raychaudhuri
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: In the Vision-and-Language Navigation (VLN) task an embodied agent navigates a 3D environment, following natural language instructions. A challenge in this task is how to handle \textquoteleftoff the path' scenarios where an agent veers from a reference path. Prior work supervises the agent with actions based on the shortest path from the agent`s location to the goal, but such goal-oriented supervision is often not in alignment with the instruction. Furthermore, the evaluation metrics employed by prior work do not measure how much of a language instruction the agent is able to follow. In this work, we propose a simple and effective language-aligned supervision scheme, and a new metric that measures the number of sub-instructions the agent has completed during navigation.
How to leverage the multimodal EHR data for better medical prediction?
Authors: Lijun Wu Bo Yang
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Healthcare is becoming a more and more important research topic recently. With the growing data in the healthcare domain, it offers a great opportunity for deep learning to improve the quality of service and reduce costs. However, the complexity of electronic health records (EHR) data is a challenge for the application of deep learning. Specifically, the data produced in the hospital admissions are monitored by the EHR system, which includes structured data like daily body temperature and unstructured data like free text and laboratory measurements. Although there are some preprocessing frameworks proposed for specific EHR data, the clinical notes that contain significant clinical value are beyond the realm of their consideration. Besides, whether these different data from various views are all beneficial to the medical tasks and how to best utilize these data remain unclear. Therefore, in this paper, we first extract the accompanying clinical notes from EHR and propose a method to integrate these data, we also comprehensively study the different models and the data leverage methods for better medical task prediction performance. The results on two prediction tasks show that our fused model with different data outperforms the state-of-the-art method without clinical notes, which illustrates the importance of our fusion method and the clinical note features.
Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer
Authors: Naoki Kobayashi, Hidetaka Kamigaito, Manabu Okumura Jingun Kwon
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents. However, constructing a coherent and informative summary is difficult using a pre-trained BERT-based encoder since it is not explicitly trained for representing the information of sentences in a document. We propose a nested tree-based extractive summarization model on RoBERTa (NeRoBERTa), where nested tree structures consist of syntactic and discourse trees in a given document. Experimental results on the CNN/DailyMail dataset showed that NeRoBERTa outperforms baseline models in ROUGE. Human evaluation results also showed that NeRoBERTa achieves significantly better scores than the baselines in terms of coherence and yields comparable scores to the state-of-the-art models.
Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization
Authors: Shaoru Guo, Ru Li, Xiaoli Li, Hongye Tan Yong Guan
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Sentence-level extractive text summarization aims to select important sentences from a given document. However, it is very challenging to model the importance of sentences. In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model sentences from both intra-sentence level and inter-sentence level, facilitating the text summarization task. In particular, intra-sentence level semantics leverage Frames and Frame Elements to model internal semantic structure within a sentence, while inter-sentence level semantics leverage Frame-to-Frame relations to model relationships among sentences. Extensive experiments on two benchmark corpus CNN/DM and NYT demonstrate that our model outperforms six state-of-the-art methods significantly.
CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees
Authors: Yanlin Wang, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, Hongbin Sun Ensheng Shi
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Code summarization aims to generate concise natural language descriptions of source code, which can help improve program comprehension and maintenance. Recent studies show that syntactic and structural information extracted from abstract syntax trees (ASTs) is conducive to summary generation. However, existing approaches fail to fully capture the rich information in ASTs because of the large size/depth of ASTs. In this paper, we propose a novel model CAST that hierarchically splits and reconstructs ASTs. First, we hierarchically split a large AST into a set of subtrees and utilize a recursive neural network to encode the subtrees. Then, we aggregate the embeddings of subtrees by reconstructing the split ASTs to get the representation of the complete AST. Finally, AST representation, together with source code embedding obtained by a vanilla code token encoder, is used for code summarization. Extensive experiments, including the ablation study and the human evaluation, on benchmarks have demonstrated the power of CAST. To facilitate reproducibility, our code and data are available at r̆lhttps://github.com/DeepSoftwareAnalytics/CAST.
SgSum:Transforming Multi-document Summarization into Sub-graph Selection
Authors: Wei Li, Jiachen Liu, Xinyan Xiao, Hua Wu, Haifeng Wang Moye Chen
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Most of existing extractive multi-document summarization (MDS) methods score each sentence individually and extract salient sentences one by one to compose a summary, which have two main drawbacks: (1) neglecting both the intra and cross-document relations between sentences; (2) neglecting the coherence and conciseness of the whole summary. In this paper, we propose a novel MDS framework (SgSum) to formulate the MDS task as a sub-graph selection problem, in which source documents are regarded as a relation graph of sentences (e.g., similarity graph or discourse graph) and the candidate summaries are its sub-graphs. Instead of selecting salient sentences, SgSum selects a salient sub-graph from the relation graph as the summary. Comparing with traditional methods, our method has two main advantages: (1) the relations between sentences are captured by modeling both the graph structure of the whole document set and the candidate sub-graphs; (2) directly outputs an integrate summary in the form of sub-graph which is more informative and coherent. Extensive experiments on MultiNews and DUC datasets show that our proposed method brings substantial improvements over several strong baselines. Human evaluation results also demonstrate that our model can produce significantly more coherent and informative summaries compared with traditional MDS methods. Moreover, the proposed architecture has strong transfer ability from single to multi-document input, which can reduce the resource bottleneck in MDS tasks.
Event Graph based Sentence Fusion
Authors: Zili Wang, Wenjie Li Ruifeng Yuan
Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (Main Conference Track), 2021

Abstract: Sentence fusion is a conditional generation task that merges several related sentences into a coherent one, which can be deemed as a summary sentence. The importance of sentence fusion has long been recognized by communities in natural language generation, especially in text summarization. It remains challenging for a state-of-the-art neural abstractive summarization model to generate a well-integrated summary sentence. In this paper, we explore the effective sentence fusion method in the context of text summarization. We propose to build an event graph from the input sentences to effectively capture and organize related events in a structured way and use the constructed event graph to guide sentence fusion. In addition to make use of the attention over the content of sentences and graph nodes, we further develop a graph flow attention mechanism to control the fusion process via the graph structure. When evaluated on sentence fusion data built from two summarization datasets, CNN/DaliyMail and Multi-News, our model shows to achieve state-of-the-art performance in terms of Rouge and other metrics like fusion rate and faithfulness.