pwc-archive/papers-with-abstracts huggingface dataset:
Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition
Authors: Yingxue Zhang, Fandong Meng, Peng Li, Ping Jian, Jie zhou
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Relation, Sentence

Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse. Existing models fail to fully utilize the contextual information which plays an important role in interpreting each local sentence. In this paper, we thus propose a novel graph-based Context Tracking Network (CT-Net) to model the discourse context for IDRR. The CT-Net firstly converts the discourse into the paragraph association graph (PAG), where each sentence tracks their closely related context from the intricate discourse through different types of edges. Then, the CT-Net extracts contextual representation from the PAG through a specially designed cross-grained updating mechanism, which can effectively integrate both sentence-level and token-level contextual semantics. Experiments on PDTB 2.0 show that the CT-Net gains better performance than models that roughly model the context.
Improving Neural RST Parsing Model with Silver Agreement Subtrees
Authors: Naoki Kobayashi, Tsutomu Hirao, Hidetaka Kamigaito, Manabu Okumura, Masaaki Nagata
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Discourse Parsing, Relation

Most of the previous Rhetorical Structure Theory (RST) parsing methods are based on supervised learning such as neural networks, that require an annotated corpus of sufficient size and quality. However, the RST Discourse Treebank (RST-DT), the benchmark corpus for RST parsing in English, is small due to the costly annotation of RST trees. The lack of large annotated training data causes poor performance especially in relation labeling. Therefore, we propose a method for improving neural RST parsing models by exploiting silver data, i.e., automatically annotated data. We create large-scale silver data from an unlabeled corpus by using a state-of-the-art RST parser. To obtain high-quality silver data, we extract agreement subtrees from RST trees for documents built using the RST parsers. We then pre-train a neural RST parser with the obtained silver data and fine-tune it on the RST-DT. Experimental results show that our method achieved the best micro-F1 scores for Nuclearity and Relation at 75.0 and 63.2, respectively. Furthermore, we obtained a remarkable gain in the Relation score, 3.0 points, against the previous state-of-the-art parser.
Bridging Resolution: Making Sense of the State of the Art
Authors: Hideo Kobayashi, Vincent Ng
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Bridging Anaphora Resolution, Multi-Task Learning

While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e.g., are the two approaches making similar or complementary mistakes?) and (2) what should be improved. To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model.
Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study
Authors: Chongyang Tao, Shen Gao, Juntao Li, Yansong Feng, Dongyan Zhao, Rui Yan
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Sentence

Sequential information, a.k.a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders. However, is it possible to encode natural languages without orders? Given a bag of words from a disordered sentence, humans may still be able to understand what those words mean by reordering or reconstructing them. Inspired by such an intuition, in this paper, we perform a study to investigate how {``}order{''} information takes effects in natural language learning. By running comprehensive comparisons, we quantitatively compare the ability of several representative neural models to organize sentences from a bag of words under three typical scenarios, and summarize some empirical findings and challenges, which can shed light on future research on this line of work.
Field Embedding: A Unified Grain-Based Framework for Word Representation
Authors: Junjie Luo, Xi Chen, Jichao Sun, Yuejia Xiang, Ningyu Zhang, Xiang Wan
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Word Embeddings

Word representations empowered with additional linguistic information have been widely studied and proved to outperform traditional embeddings. Current methods mainly focus on learning embeddings for words while embeddings of linguistic information (referred to as grain embeddings) are discarded after the learning. This work proposes a framework field embedding to jointly learn both word and grain embeddings by incorporating morphological, phonetic, and syntactical linguistic fields. The framework leverages an innovative fine-grained pipeline that integrates multiple linguistic fields and produces high-quality grain sequences for learning supreme word representations. A novel algorithm is also designed to learn embeddings for words and grains by capturing information that is contained within each field and that is shared across them. Experimental results of lexical tasks and downstream natural language processing tasks illustrate that our framework can learn better word embeddings and grain embeddings. Qualitative evaluations show grain embeddings effectively capture the semantic information.
Why Do Document-Level Polarity Classifiers Fail?
Authors: Karen Martins, Pedro O.S Vaz-de-Melo, Rodrygo Santos
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4

Machine learning solutions are often criticized for the lack of explanation of their successes and failures. Understanding which instances are misclassified and why is essential to improve the learning process. This work helps to fill this gap by proposing a methodology to characterize, quantify and measure the impact of hard instances in the task of polarity classification of movie reviews. We characterize such instances into two categories: neutrality, where the text does not convey a clear polarity, and discrepancy, where the polarity of the text is the opposite of its true rating. We quantify the number of hard instances in polarity classification of movie reviews and provide empirical evidence about the need to pay attention to such problematic instances, as they are much harder to classify, for both machine and human classifiers. To the best of our knowledge, this is the first systematic analysis of the impact of hard instances in polarity detection from well-formed textual reviews.
A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents
Authors: Qingrong Xia, Bo Zhang, Rui Wang, Zhenghua Li, Yue Zhang, Fei Huang, Luo Si, Min Zhang
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Multi-Task Learning, Opinion Mining, Sentence

Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of {``}Who expressed what opinions towards what{''} in one sentence. In this work, motivated by its span-based representations of opinion expressions and roles, we propose a unified span-based approach for the end-to-end OM setting. Furthermore, inspired by the unified span-based formalism of OM and constituent parsing, we explore two different methods (multi-task learning and graph convolutional neural network) to integrate syntactic constituents into the proposed model to help OM. We conduct experiments on the commonly used MPQA 2.0 dataset. The experimental results show that our proposed unified span-based approach achieves significant improvements over previous works in the exact F1 score and reduces the number of wrongly-predicted opinion expressions and roles, showing the effectiveness of our method. In addition, incorporating the syntactic constituents achieves promising improvements over the strong baseline enhanced by contextualized word representations.
Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction
Authors: Yuhao Feng, Yanghui Rao, Yuyao Tang, Ninghua Wang, He Liu
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Language Modeling, Language Modelling, Sentiment Analysis, target-oriented opinion words extraction, Term Extraction

Opinion target extraction and opinion term extraction are two fundamental tasks in Aspect Based Sentiment Analysis (ABSA). Many recent works on ABSA focus on Target-oriented Opinion Words (or Terms) Extraction (TOWE), which aims at extracting the corresponding opinion words for a given opinion target. TOWE can be further applied to Aspect-Opinion Pair Extraction (AOPE) which aims at extracting aspects (i.e., opinion targets) and opinion terms in pairs. In this paper, we propose Target-Specified sequence labeling with Multi-head Self-Attention (TSMSA) for TOWE, in which any pre-trained language model with multi-head self-attention can be integrated conveniently. As a case study, we also develop a Multi-Task structure named MT-TSMSA for AOPE by combining our TSMSA with an aspect and opinion term extraction module. Experimental results indicate that TSMSA outperforms the benchmark methods on TOWE significantly; meanwhile, the performance of MT-TSMSA is similar or even better than state-of-the-art AOPE baseline models.
Target-Aware Data Augmentation for Stance Detection
Authors: Yingjie Li, Cornelia Caragea
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Data Augmentation, Language Modeling, Language Modelling, Masked Language Modeling, Sentence, Stance Detection

The goal of stance detection is to identify whether the author of a text is in favor of, neutral or against a specific target. Despite substantial progress on this task, one of the remaining challenges is the scarcity of annotations. Data augmentation is commonly used to address annotation scarcity by generating more training samples. However, the augmented sentences that are generated by existing methods are either less diversified or inconsistent with the given target and stance label. In this paper, we formulate the data augmentation of stance detection as a conditional masked language modeling task and augment the dataset by predicting the masked word conditioned on both its context and the auxiliary sentence that contains target and label information. Moreover, we propose another simple yet effective method that generates target-aware sentence by replacing a target mention with the other. Experimental results show that our proposed methods significantly outperforms previous augmentation methods on 11 targets.
End-to-end ASR to jointly predict transcriptions and linguistic annotations
Authors: Motoi Omachi, Yuya Fujita, Shinji Watanabe, Matthew Wiesner
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), POS, speech-recognition, Speech Recognition, Spoken Dialogue Systems, TAG, Translation

We propose a Transformer-based sequence-to-sequence model for automatic speech recognition (ASR) capable of simultaneously transcribing and annotating audio with linguistic information such as phonemic transcripts or part-of-speech (POS) tags. Since linguistic information is important in natural language processing (NLP), the proposed ASR is especially useful for speech interface applications, including spoken dialogue systems and speech translation, which combine ASR and NLP. To produce linguistic annotations, we train the ASR system using modified training targets: each grapheme or multi-grapheme unit in the target transcript is followed by an aligned phoneme sequence and/or POS tag. Since our method has access to the underlying audio data, we can estimate linguistic annotations more accurately than pipeline approaches in which NLP-based methods are applied to a hypothesized ASR transcript. Experimental results on Japanese and English datasets show that the proposed ASR system is capable of simultaneously producing high-quality transcriptions and linguistic annotations.
Worldly Wise (WoW) - Cross-Lingual Knowledge Fusion for Fact-based Visual Spoken-Question Answering
Authors: Kiran Ramnath, Leda Sari, Mark Hasegawa-Johnson, Chang Yoo
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Knowledge Graphs, Question Answering, Speech-to-Text, Spoken Language Understanding, World Knowledge

Although Question-Answering has long been of research interest, its accessibility to users through a speech interface and its support to multiple languages have not been addressed in prior studies. Towards these ends, we present a new task and a synthetically-generated dataset to do Fact-based Visual Spoken-Question Answering (FVSQA). FVSQA is based on the FVQA dataset, which requires a system to retrieve an entity from Knowledge Graphs (KGs) to answer a question about an image. In FVSQA, the question is spoken rather than typed. Three sub-tasks are proposed: (1) speech-to-text based, (2) end-to-end, without speech-to-text as an intermediate component, and (3) cross-lingual, in which the question is spoken in a language different from that in which the KG is recorded. The end-to-end and cross-lingual tasks are the first to require world knowledge from a multi-relational KG as a differentiable layer in an end-to-end spoken language understanding task, hence the proposed reference implementation is called Worldly-Wise (WoW).WoW is shown to perform end-to-end cross-lingual FVSQA at same levels of accuracy across 3 languages - English, Hindi, and Turkish.
Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network
Authors: Haoran Wu, Wei Chen, Shuang Xu, Bo Xu
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: counterfactual

Providing a reliable explanation for clinical diagnosis based on the Electronic Medical Record (EMR) is fundamental to the application of Artificial Intelligence in the medical field. Current methods mostly treat the EMR as a text sequence and provide explanations based on a precise medical knowledge base, which is disease-specific and difficult to obtain for experts in reality. Therefore, we propose a counterfactual multi-granularity graph supporting facts extraction (CMGE) method to extract supporting facts from irregular EMR itself without external knowledge bases in this paper. Specifically, we first structure the sequence of EMR into a hierarchical graph network and then obtain the causal relationship between multi-granularity features and diagnosis results through counterfactual intervention on the graph. Features having the strongest causal connection with the results provide interpretive support for the diagnosis. Experimental results on real Chinese EMR of the lymphedema demonstrate that our method can diagnose four types of EMR correctly, and can provide accurate supporting facts for the results. More importantly, the results on different diseases demonstrate the robustness of our approach, which represents the potential application in the medical field.
Personalized Response Generation via Generative Split Memory Network
Authors: Yuwei Wu, Xuezhe Ma, Diyi Yang
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Response Generation, Text Generation

Despite the impressive successes of generation and dialogue systems, how to endow a text generation system with particular personality traits to deliver more personalized responses remains under-investigated. In this work, we look at how to generate personalized responses for questions on Reddit by utilizing personalized user profiles and posting histories. Specifically, we release an open-domain \textit{single-turn} dialog dataset made up of 1.5M conversation pairs together with 300k profiles of users and related comments. We then propose a memory network to generate personalized responses in dialogue that utilizes a novel mechanism of splitting memories: one for user profile meta attributes and the other for user-generated information like comment histories. Experimental results show the quantitative and qualitative improvements of our simple split memory network model over the state-of-the-art response generation baselines.
Generating An Optimal Interview Question Plan Using A Knowledge Graph And Integer Linear Programming
Authors: Soham Datta, Prabir Mallick, Sangameshwar Patil, Indrajit Bhattacharya, Girish Palshikar
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Diversity

Given the diversity of the candidates and complexity of job requirements, and since interviewing is an inherently subjective process, it is an important task to ensure consistent, uniform, efficient and objective interviews that result in high quality recruitment. We propose an interview assistant system to automatically, and in an objective manner, select an optimal set of technical questions (from question banks) personalized for a candidate. This set can help a human interviewer to plan for an upcoming interview of that candidate. We formalize the problem of selecting a set of questions as an integer linear programming problem and use standard solvers to get a solution. We use knowledge graph as background knowledge in this formulation, and derive our objective functions and constraints from it. We use candidate{'}s resume to personalize the selection of questions. We propose an intrinsic evaluation to compare a set of suggested questions with actually asked questions. We also use expert interviewers to comparatively evaluate our approach with a set of reasonable baselines.
A Global Past-Future Early Exit Method for Accelerating Inference of Pre-trained Language Models
Authors: Kaiyuan Liao, Yi Zhang, Xuancheng Ren, Qi Su, Xu sun, Bin He
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4

Early exit mechanism aims to accelerate the inference speed of large-scale pre-trained language models. The essential idea is to exit early without passing through all the inference layers at the inference stage. To make accurate predictions for downstream tasks, the hierarchical linguistic information embedded in all layers should be jointly considered. However, much of the research up to now has been limited to use local representations of the exit layer. Such treatment inevitably loses information of the unused past layers as well as the high-level features embedded in future layers, leading to sub-optimal performance. To address this issue, we propose a novel Past-Future method to make comprehensive predictions from a global perspective. We first take into consideration all the linguistic information embedded in the past layers and then take a further step to engage the future information which is originally inaccessible for predictions. Extensive experiments demonstrate that our method outperforms previous early exit methods by a large margin, yielding better and robust performance.
Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data
Authors: Chenghao Jia, Yongliang Shen, Yechun Tang, Lu Sun, Weiming Lu
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Relation

Prerequisite relations among concepts are crucial for educational applications, such as curriculum planning and intelligent tutoring. In this paper, we propose a novel concept prerequisite relation learning approach, named CPRL, which combines both concept representation learned from a heterogeneous graph and concept pairwise features. Furthermore, we extend CPRL under weakly supervised settings to make our method more practical, including learning prerequisite relations from learning object dependencies and generating training data with data programming. Our experiments on four datasets show that the proposed approach achieves the state-of-the-art results comparing with existing methods.
WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations
Authors: Tomoyuki Kajiwara, Chenhui Chu, Noriko Takemura, Yuta Nakashima, Hajime Nagahara
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Emotion Recognition

We annotate 17,000 SNS posts with both the writer{'}s subjective emotional intensity and the reader{'}s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer{'}s subjective labels than the readers{'}. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.
Automatic Classification of Neutralization Techniques in the Narrative of Climate Change Scepticism
Authors: Shraey Bhatia, Jey Han Lau, Timothy Baldwin
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4

Neutralisation techniques, e.g. denial of responsibility and denial of victim, are used in the narrative of climate change scepticism to justify lack of action or to promote an alternative view. We first draw on social science to introduce the problem to the community of nlp, present the granularity of the coding schema and then collect manual annotations of neutralised techniques in text relating to climate change, and experiment with supervised and semi- supervised BERT-based models.
Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning
Authors: Ramit Sawhney, Harshit Joshi, Rajiv Ratn Shah, Lucie Flek
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4

Recent psychological studies indicate that individuals exhibiting suicidal ideation increasingly turn to social media rather than mental health practitioners. Personally contextualizing the buildup of such ideation is critical for accurate identification of users at risk. In this work, we propose a framework jointly leveraging a user{'}s emotional history and social information from a user{'}s neighborhood in a network to contextualize the interpretation of the latest tweet of a user on Twitter. Reflecting upon the scale-free nature of social network relationships, we propose the use of Hyperbolic Graph Convolution Networks, in combination with the Hawkes process to learn the historical emotional spectrum of a user in a time-sensitive manner. Our system significantly outperforms state-of-the-art methods on this task, showing the benefits of both socially and personally contextualized representations.