pwc-archive/papers-with-abstracts huggingface dataset:
Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis
Authors: Bin Liang, Rongdi Yin, Lin Gui, Jiachen Du, Ruifeng Xu
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Sentence, Sentiment Analysis

In this paper, we explore a novel solution of constructing a heterogeneous graph for each instance by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect and propose an Interactive Graph Convolutional Networks (InterGCN) model for aspect sentiment analysis. Specifically, an ordinary dependency graph is first constructed for each sentence over the dependency tree. Then we refine the graph by considering the syntactical dependencies between contextual words and aspect-specific words to derive the aspect-focused graph. Subsequently, the aspect-focused graph and the corresponding embedding matrix are fed into the aspect-focused GCN to capture the key aspect and contextual words. Besides, to interactively extract the inter-aspect relations for the specific aspect, an inter-aspect GCN is adopted to model the representations learned by aspect-focused GCN based on the inter-aspect graph which is constructed by the relative dependencies between the aspect words and other aspects. Hence, the model can be aware of the significant contextual and aspect words when interactively learning the sentiment features for a specific aspect. Experimental results on four benchmark datasets illustrate that our proposed model outperforms state-of-the-art methods and substantially boosts the performance in comparison with BERT.
METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis
Authors: Bin Jiang, Jing Hou, Wanyue Zhou, Chao Yang, Shihan Wang, Liang Pang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Representation Learning, Sentence, Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of each specific aspect in a given sentence. Existing researches have realized the importance of the aspect for the ABSA task and have derived many interactive learning methods that model context based on specific aspect. However, current interaction mechanisms are ill-equipped to learn complex sentences with multiple aspects, and these methods underestimate the representation learning of the aspect. In order to solve the two problems, we propose a mutual enhanced transformation network (METNet) for the ABSA task. First, the aspect enhancement module in METNet improves the representation learning of the aspect with contextual semantic features, which gives the aspect more abundant information. Second, METNet designs and implements a hierarchical structure, which enhances the representations of aspect and context iteratively. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of METNet, and we further prove that METNet is outstanding in multi-aspect scenarios.
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network
Authors: Ying Chen, Wenjun Hou, Shoushan Li, Caicong Wu, Xiaoqiang Zhang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages. Considering that most emotions usually have few causes mentioned in their contexts, we present a novel end-to-end Pair Graph Convolutional Network (PairGCN) to model pair-level contexts so that to capture the dependency information among local neighborhood candidate pairs. Moreover, in the graphical network, contexts are grouped into three types and each type of contexts is propagated by its own way. Experiments on a benchmark Chinese emotion-cause pair extraction corpus demonstrate the effectiveness of the proposed model.
A Unified Sequence Labeling Model for Emotion Cause Pair Extraction
Authors: Xinhong Chen, Qing Li, JianPing Wang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE) aims at extracting emotions and causes as pairs from documents, where each pair contains an emotion clause and a set of cause clauses. Existing approaches address the task by first extracting emotion and cause clauses via two binary classifiers separately, and then training another binary classifier to pair them up. However, the extracted emotion-cause pairs of different emotion types cannot be distinguished from each other through simple binary classifiers, which limits the applicability of the existing approaches. Moreover, such two-step approaches may suffer from possible cascading errors. In this paper, to address the first problem, we assign emotion type labels to emotion and cause clauses so that emotion-cause pairs of different emotion types can be easily distinguished. As for the second problem, we reformulate the ECPE task as a unified sequence labeling task, which can extract multiple emotion-cause pairs in an end-to-end fashion. We propose an approach composed of a convolution neural network for encoding neighboring information and two Bidirectional Long-Short Term Memory networks for two auxiliary tasks. Experiment results demonstrate the feasibility and effectiveness of our approaches.
Regrexit or not Regrexit: Aspect-based Sentiment Analysis in Polarized Contexts
Authors: Vorakit Vorakitphan, Marco Guerini, Elena Cabrio, Serena Villata
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Emotion Recognition, Sentence, Sentiment Analysis

Emotion analysis in polarized contexts represents a challenge for Natural Language Processing modeling. As a step in the aforementioned direction, we present a methodology to extend the task of Aspect-based Sentiment Analysis (ABSA) toward the affect and emotion representation in polarized settings. In particular, we adopt the three-dimensional model of affect based on Valence, Arousal, and Dominance (VAD). We then present a Brexit scenario that proves how affect varies toward the same aspect when politically polarized stances are presented. Our approach captures aspect-based polarization from newspapers regarding the Brexit scenario of 1.2m entities at sentence-level. We demonstrate how basic constituents of emotions can be mapped to the VAD model, along with their interactions respecting the polarized context in ABSA settings using biased key-concepts (e.g., {``}stop Brexit{''} vs. {``}support Brexit{''}). Quite intriguingly, the framework achieves to produce coherent aspect evidences of Brexit{'}s stance from key-concepts, showing that VAD influence the support and opposition aspects.
Affective and Contextual Embedding for Sarcasm Detection
Authors: Nastaran Babanejad, Heidar Davoudi, Aijun An, Manos Papagelis
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Sarcasm Detection, Word Embeddings

Automatic sarcasm detection from text is an important classification task that can help identify the actual sentiment in user-generated data, such as reviews or tweets. Despite its usefulness, sarcasm detection remains a challenging task, due to a lack of any vocal intonation or facial gestures in textual data. To date, most of the approaches to addressing the problem have relied on hand-crafted affect features, or pre-trained models of non-contextual word embeddings, such as Word2vec. However, these models inherit limitations that render them inadequate for the task of sarcasm detection. In this paper, we propose two novel deep neural network models for sarcasm detection, namely ACE 1 and ACE 2. Given as input a text passage, the models predict whether it is sarcastic (or not). Our models extend the architecture of BERT by incorporating both affective and contextual features. To the best of our knowledge, this is the first attempt to directly alter BERT{'}s architecture and train it from scratch to build a sarcasm classifier. Extensive experiments on different datasets demonstrate that the proposed models outperform state-of-the-art models for sarcasm detection with significant margins.
Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks
Authors: Guimin Chen, Yuanhe Tian, Yan Song
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Extraction, Sentence, Sentiment Analysis

End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first extracts the aspect terms in a sentence and the second predicts the sentiment polarities for such terms. For EASA, compared to pipeline and multi-task approaches, joint aspect extraction and sentiment analysis provides a one-step solution to predict both aspect terms and their sentiment polarities through a single decoding process, which avoid the mismatches in between the results of aspect terms and sentiment polarities, as well as error propagation. Previous studies, especially recent ones, for this task focus on using powerful encoders (e.g., Bi-LSTM and BERT) to model contextual information from the input, with limited efforts paid to using advanced neural architectures (such as attentions and graph convolutional networks) or leveraging extra knowledge (such as syntactic information). To extend such efforts, in this paper, we propose directional graph convolutional networks (D-GCN) to jointly perform aspect extraction and sentiment analysis with encoding syntactic information, where dependency among words are integrated in our model to enhance its ability of representing input sentences and help EASA accordingly. Experimental results on three benchmark datasets demonstrate the effectiveness of our approach, where D-GCN achieves state-of-the-art performance on all datasets.
Train Once, and Decode As You Like
Authors: Chao Tian, Yifei Wang, Hao Cheng, Yijiang Lian, Zhihua Zhang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Machine Translation, Translation

In this paper we propose a unified approach for supporting different generation manners of machine translation, including autoregressive, semi-autoregressive, and refinement-based non-autoregressive models. Our approach works by repeatedly selecting positions and generating tokens at these selected positions. After being trained once, our approach achieves better or competitive translation performance compared with some strong task-specific baseline models in all the settings. This generalization ability benefits mainly from the new training objective that we propose. We validate our approach on the WMT{'}14 English-German and IWSLT{'}14 German-English translation tasks. The experimental results are encouraging.
A Representation Learning Approach to Animal Biodiversity Conservation
Authors: Meet Mukadam, Mandhara Jayaram, Yongfeng Zhang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Representation Learning

Generating knowledge from natural language data has aided in solving many artificial intelligence problems. Vector representations of words have been the driving force behind the majority of natural language processing tasks. This paper develops a novel approach for predicting the conservation status of animal species using custom generated scientific name embeddings. We use two different vector embeddings generated using representation learning on Wikipedia text and animal taxonomy data. We generate name embeddings for all species in the animal kingdom using unsupervised learning and build a model on the IUCN Red List dataset to classify species into endangered or least-concern. To our knowledge, this is the first work that makes use of learnt features instead of handcrafted features for this task and achieves competitive results. Based on the high confidence results of our model, we also predict the conservation status of data deficient species whose conservation status is still unknown and thus steering more focus towards them for protection. These embeddings have also been made publicly available here. We believe this will greatly help in solving various downstream tasks and further advance research in the cross-domain involving natural language processing, conservation biology, and life sciences.
Integrating External Event Knowledge for Script Learning
Authors: Shangwen Lv, Fuqing Zhu, Songlin Hu
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Retrieval

Script learning aims to predict the subsequent event according to the existing event chain. Recent studies focus on event co-occurrence to solve this problem. However, few studies integrate external event knowledge to solve this problem. With our observations, external event knowledge can provide additional knowledge like temporal or causal knowledge for understanding event chain better and predicting the right subsequent event. In this work, we integrate event knowledge from ASER (Activities, States, Events and their Relations) knowledge base to help predict the next event. We propose a new approach consisting of knowledge retrieval stage and knowledge integration stage. In the knowledge retrieval stage, we select relevant external event knowledge from ASER. In the knowledge integration stage, we propose three methods to integrate external knowledge into our model and infer final answers. Experiments on the widely-used Multi- Choice Narrative Cloze (MCNC) task show our approach achieves state-of-the-art performance compared to other methods.
Heterogeneous Graph Neural Networks to Predict What Happen Next
Authors: Jianming Zheng, Fei Cai, Yanxiang Ling, Honghui Chen
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8

Given an incomplete event chain, script learning aims to predict the missing event, which can support a series of NLP applications. Existing work cannot well represent the heterogeneous relations and capture the discontinuous event segments that are common in the event chain. To address these issues, we introduce a heterogeneous-event (HeterEvent) graph network. In particular, we employ each unique word and individual event as nodes in the graph, and explore three kinds of edges based on realistic relations (e.g., the relations of word-and-word, word-and-event, event-and-event). We also design a message passing process to realize information interactions among homo or heterogeneous nodes. And the discontinuous event segments could be explicitly modeled by finding the specific path between corresponding nodes in the graph. The experimental results on one-step and multi-step inference tasks demonstrate that our ensemble model HeterEvent[W+E] can outperform existing baselines.
SQL Generation via Machine Reading Comprehension
Authors: Zeyu Yan, Jianqiang Ma, Yang Zhang, Jianping Shen
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Machine Reading Comprehension, Question Answering, Reading Comprehension, slot-filling, Slot Filling, Text to SQL, Text-To-SQL

Text-to-SQL systems offers natural language interfaces to databases, which can automatically generates SQL queries given natural language questions. On the WikiSQL benchmark, state-of- the-art text-to-SQL systems typically take a slot-filling approach by building several specialized models for each type of slot. Despite being effective, such modularized systems are complex and also fall short in jointly learning for different slots. To solve these problems, this paper proposes a novel approach that formulates the task as a question answering problem, where different slots are predicted by a unified machine reading comprehension (MRC) model. For this purpose, we use a BERT-based MRC model, which can also benefit from intermediate training on other MRC datasets. The proposed method can achieve competitive results on WikiSQL, suggesting it being a promising direction for text-to-SQL.
Towards Privacy by Design in Learner Corpora Research: A Case of On-the-fly Pseudonymization of Swedish Learner Essays
Authors: Elena Volodina, Yousuf Ali Mohammed, Sandra Derbring, Arild Matsson, Beata Megyesi
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8

This article reports on an ongoing project aiming at automatization of pseudonymization of learner essays. The process includes three steps: identification of personal information in an unstructured text, labeling for a category, and pseudonymization. We experiment with rule-based methods for detection of 15 categories out of the suggested 19 (Megyesi et al., 2018) that we deem important and/or doable with automatic approaches. For the detection and labeling steps,we use resources covering personal names, geographic names, company and university names and others. For the pseudonymization step, we replace the item using another item of the same type from the above-mentioned resources. Evaluation of the detection and labeling steps are made on a set of manually anonymized essays. The results are promising and show that 89{\%} of the personal information can be successfully identified in learner data, and annotated correctly with an inter-annotator agreement of 86{\%} measured as Fleiss kappa and Krippendorff{'}s alpha.
PG-GSQL: Pointer-Generator Network with Guide Decoding for Cross-Domain Context-Dependent Text-to-SQL Generation
Authors: Huajie Wang, Mei Li, Lei Chen
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Decoder, Text to SQL, Text-To-SQL

Text-to-SQL is a task of translating utterances to SQL queries, and most existing neural approaches of text-to-SQL focus on the cross-domain context-independent generation task. We pay close attention to the cross-domain context-dependent text-to-SQL generation task, which requires a model to depend on the interaction history and current utterance to generate SQL query. In this paper, we present an encoder-decoder model called PG-GSQL based on the interaction-level encoder and with two effective innovations in decoder to solve cross-domain context-dependent text-to-SQL task. 1) To effectively capture historical information of SQL query and reuse the previous SQL query tokens, we use a hybrid pointer-generator network as decoder to copy tokens from the previous SQL query via pointer, the generator part is utilized to generate new tokens. 2) We propose a guide component to limit the prediction space of vocabulary for avoiding table-column dependency and foreign key dependency errors during decoding phase. In addition, we design a column-table linking mechanism to improve the prediction accuracy of tables. On the challenging cross-domain context-dependent text-to-SQL benchmark SParC, PG-GSQL achieves 34.0{\%} question matching accuracy and 19.0{\%} interaction matching accuracy on the dev set. With BERT augmentation, PG-GSQL obtains 53.1{\%} question matching accuracy and 34.7{\%} interaction matching accuracy on the dev set, outperforms the previous state-of-the-art model by 5.9{\%} question matching accuracy and 5.2{\%} interaction matching accuracy. Our code is publicly available.
Neural Approaches for Natural Language Interfaces to Databases: A Survey
Authors: Radu Cristian Alexandru Iacob, Florin Brad, Elena-Simona Apostol, Ciprian-Octavian Truic{\u{a}}, Ionel Alexandru Hosu, Traian Rebedea
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Decoder, Survey

A natural language interface to databases (NLIDB) enables users without technical expertise to easily access information from relational databases. Interest in NLIDBs has resurged in the past years due to the availability of large datasets and improvements to neural sequence-to-sequence models. In this survey we focus on the key design decisions behind current state of the art neural approaches, which we group into encoder and decoder improvements. We highlight the three most important directions, namely linking question tokens to database schema elements (schema linking), better architectures for encoding the textual query taking into account the schema (schema encoding), and improved generation of structured queries using autoregressive neural models (grammar-based decoders). To foster future research, we also present an overview of the most important NLIDB datasets, together with a comparison of the top performing neural models and a short insight into recent non deep learning solutions.
Predicting Stance Change Using Modular Architectures
Authors: Aldo Porco, Dan Goldwasser
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8

The ability to change a person{'}s mind on a given issue depends both on the arguments they are presented with and on their underlying perspectives and biases on that issue. Predicting stance changes require characterizing both aspects and the interaction between them, especially in realistic settings in which stance changes are very rare. In this paper, we suggest a modular learning approach, which decomposes the task into multiple modules, focusing on different aspects of the interaction between users, their beliefs, and the arguments they are exposed to. Our experiments show that our modular approach archives significantly better results compared to the end-to-end approach using BERT over the same inputs.
Leveraging HTML in Free Text Web Named Entity Recognition
Authors: Colin Ashby, David Weir
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Text Segmentation

HTML tags are typically discarded in free text Named Entity Recognition from Web pages. We investigate whether these discarded tags might be used to improve NER performance. We compare Text+Tags sentences with their Text-Only equivalents, over five datasets, two free text segmentation granularities and two NER models. We find an increased F1 performance for Text+Tags of between 0.9{\%} and 13.2{\%} over all datasets, variants and models. This performance increase, over datasets of varying entity types, HTML density and construction quality, indicates our method is flexible and adaptable. These findings imply that a similar technique might be of use in other Web-aware NLP tasks, including the enrichment of deep language models.
Multimodal Review Generation with Privacy and Fairness Awareness
Authors: Xuan-Son Vu, Thanh-Son Nguyen, Duc-Trong Le, Lili Jiang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Fairness, Review Generation, Sentiment Analysis, Word Embeddings

Users express their opinions towards entities (e.g., restaurants) via online reviews which can be in diverse forms such as text, ratings, and images. Modeling reviews are advantageous for user behavior understanding which, in turn, supports various user-oriented tasks such as recommendation, sentiment analysis, and review generation. In this paper, we propose MG-PriFair, a multimodal neural-based framework, which generates personalized reviews with privacy and fairness awareness. Motivated by the fact that reviews might contain personal information and sentiment bias, we propose a novel differentially private (dp)-embedding model for training privacy guaranteed embeddings and an evaluation approach for sentiment fairness in the food-review domain. Experiments on our novel review dataset show that MG-PriFair is capable of generating plausibly long reviews while controlling the amount of exploited user data and using the least sentiment biased word embeddings. To the best of our knowledge, we are the first to bring user privacy and sentiment fairness into the review generation task. The dataset and source codes are available at https://github.com/ReML-AI/MG-PriFair.