pwc-archive/papers-with-abstracts huggingface dataset:
Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network
Authors: Hongjie Cai, Yaofeng Tu, Xiangsheng Zhou, Jianfei Yu, Rui Xia
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Category Detection, Sentiment Analysis, Sentiment Classification

Most of the aspect based sentiment analysis research aims at identifying the sentiment polarities toward some explicit aspect terms while ignores implicit aspects in text. To capture both explicit and implicit aspects, we focus on aspect-category based sentiment analysis, which involves joint aspect category detection and category-oriented sentiment classification. However, currently only a few simple studies have focused on this problem. The shortcomings in the way they defined the task make their approaches difficult to effectively learn the inner-relations between categories and the inter-relations between categories and sentiments. In this work, we re-formalize the task as a category-sentiment hierarchy prediction problem, which contains a hierarchy output structure to first identify multiple aspect categories in a piece of text, and then predict the sentiment for each of the identified categories. Specifically, we propose a Hierarchical Graph Convolutional Network (Hier-GCN), where a lower-level GCN is to model the inner-relations among multiple categories, and the higher-level GCN is to capture the inter-relations between aspect categories and sentiments. Extensive evaluations demonstrate that our hierarchy output structure is superior over existing ones, and the Hier-GCN model can consistently achieve the best results on four benchmarks.
Constituency Lattice Encoding for Aspect Term Extraction
Authors: Yunyi Yang, Kun Li, Xiaojun Quan, Weizhou Shen, Qinliang Su
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Aspect Term Extraction and Sentiment Classification, Sentence, Sentiment Analysis, Term Extraction

One of the remaining challenges for aspect term extraction in sentiment analysis resides in the extraction of phrase-level aspect terms, which is non-trivial to determine the boundaries of such terms. In this paper, we aim to address this issue by incorporating the span annotations of constituents of a sentence to leverage the syntactic information in neural network models. To this end, we first construct a constituency lattice structure based on the constituents of a constituency tree. Then, we present two approaches to encoding the constituency lattice using BiLSTM-CRF and BERT as the base models, respectively. We experimented on two benchmark datasets to evaluate the two models, and the results confirm their superiority with respective 3.17 and 1.35 points gained in F1-Measure over the current state of the art. The improvements justify the effectiveness of the constituency lattice for aspect term extraction.
Do Word Embeddings Capture Spelling Variation?
Authors: Dong Nguyen, Jack Grieve
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Word Embeddings

Analyses of word embeddings have primarily focused on semantic and syntactic properties. However, word embeddings have the potential to encode other properties as well. In this paper, we propose a new perspective on the analysis of word embeddings by focusing on spelling variation. In social media, spelling variation is abundant and often socially meaningful. Here, we analyze word embeddings trained on Twitter and Reddit data. We present three analyses using pairs of word forms covering seven types of spelling variation in English. Taken together, our results show that word embeddings encode spelling variation patterns of various types to some extent, even embeddings trained using the skipgram model which does not take spelling into account. Our results also suggest a link between the intentionality of the variation and the distance of the non-conventional spellings to their conventional spellings.
Don't take ``nswvtnvakgxpm'' for an answer --The surprising vulnerability of automatic content scoring systems to adversarial input
Authors: Yuning Ding, Brian Riordan, Andrea Horbach, Aoife Cahill, Torsten Zesch
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8

Automatic content scoring systems are widely used on short answer tasks to save human effort. However, the use of these systems can invite cheating strategies, such as students writing irrelevant answers in the hopes of gaining at least partial credit. We generate adversarial answers for benchmark content scoring datasets based on different methods of increasing sophistication and show that even simple methods lead to a surprising decrease in content scoring performance. As an extreme example, up to 60{\%} of adversarial answers generated from random shuffling of words in real answers are accepted by a state-of-the-art scoring system. In addition to analyzing the vulnerabilities of content scoring systems, we examine countermeasures such as adversarial training and show that these measures improve system robustness against adversarial answers considerably but do not suffice to completely solve the problem.
Automated Prediction of Examinee Proficiency from Short-Answer Questions
Authors: Le An Ha, Victoria Yaneva, Polina Harik, Ravi Pandian, Amy Morales, Brian Clauser
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Multiple-choice, Prediction

This paper brings together approaches from the fields of NLP and psychometric measurement to address the problem of predicting examinee proficiency from responses to short-answer questions (SAQs). While previous approaches train on manually labeled data to predict the human-ratings assigned to SAQ responses, the approach presented here models examinee proficiency directly and does not require manually labeled data to train on. We use data from a large medical exam where experimental SAQ items are embedded alongside 106 scored multiple-choice questions (MCQs). First, the latent trait of examinee proficiency is measured using the scored MCQs and then a model is trained on the experimental SAQ responses as input, aiming to predict proficiency as its target variable. The predicted value is then used as a {``}score{''} for the SAQ response and evaluated in terms of its contribution to the precision of proficiency estimation.
A Dataset and Evaluation Framework for Complex Geographical Description Parsing
Authors: Egoitz Laparra, Steven Bethard
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8

Much previous work on geoparsing has focused on identifying and resolving individual toponyms in text like Adrano, S.Maria di Licodia or Catania. However, geographical locations occur not only as individual toponyms, but also as compositions of reference geolocations joined and modified by connectives, e.g., {``}. . . between the towns of Adrano and S.Maria di Licodia, 32 kilometres northwest of Catania{''}. Ideally, a geoparser should be able to take such text, and the geographical shapes of the toponyms referenced within it, and parse these into a geographical shape, formed by a set of coordinates, that represents the location described. But creating a dataset for this complex geoparsing task is difficult and, if done manually, would require a huge amount of effort to annotate the geographical shapes of not only the geolocation described but also the reference toponyms. We present an approach that automates most of the process by combining Wikipedia and OpenStreetMap. As a result, we have gathered a collection of 360,187 uncurated complex geolocation descriptions, from which we have manually curated 1,000 examples intended to be used as a test set. To accompany the data, we define a new geoparsing evaluation framework along with a scoring methodology and a set of baselines.
Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain
Authors: Dongmin Hyun, Junsu Cho, Hwanjo Yu
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Sentiment Analysis

We release large-scale datasets of users{'} comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain. The datasets consist of 58,000+ commentaspect pairs, which are the largest compared to existing datasets. In addition, this work covers new language (i.e., Korean) along with English for aspect-level sentiment analysis. We build the datasets from automotive domain to enable users (e.g., marketers in automotive companies) to analyze the voice of customers on automobiles. We also provide baseline performances for future work by evaluating recent models on the released datasets.
A High Precision Pipeline for Financial Knowledge Graph Construction
Authors: Sarah Elhammadi, Laks V.S. Lakshmanan, Raymond Ng, Michael Simpson, Baoxing Huai, Zhefeng Wang, Lanjun Wang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Articles, Data Integration, Fact Checking, graph construction, Knowledge Graphs, Question Answering, Vocal Bursts Intensity Prediction

Motivated by applications such as question answering, fact checking, and data integration, there is significant interest in constructing knowledge graphs by extracting information from unstructured information sources, particularly text documents. Knowledge graphs have emerged as a standard for structured knowledge representation, whereby entities and their inter-relations are represented and conveniently stored as (subject,predicate,object) triples in a graph that can be used to power various downstream applications. The proliferation of financial news sources reporting on companies, markets, currencies, and stocks presents an opportunity for extracting valuable knowledge about this crucial domain. In this paper, we focus on constructing a knowledge graph automatically by information extraction from a large corpus of financial news articles. For that purpose, we develop a high precision knowledge extraction pipeline tailored for the financial domain. This pipeline combines multiple information extraction techniques with a financial dictionary that we built, all working together to produce over 342,000 compact extractions from over 288,000 financial news articles, with a precision of 78{\%} at the top-100 extractions.The extracted triples are stored in a knowledge graph making them readily available for use in downstream applications.
Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets
Authors: Frank Xing, Lorenzo Malandri, Yue Zhang, Erik Cambria
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Autonomous Driving, Cultural Vocal Bursts Intensity Prediction, Sentiment Analysis

The recent dominance of machine learning-based natural language processing methods has fostered the culture of overemphasizing model accuracies rather than studying the reasons behind their errors. Interpretability, however, is a critical requirement for many downstream AI and NLP applications, e.g., in finance, healthcare, and autonomous driving. This study, instead of proposing any {``}new model{''}, investigates the error patterns of some widely acknowledged sentiment analysis methods in the finance domain. We discover that (1) those methods belonging to the same clusters are prone to similar error patterns, and (2) there are six types of linguistic features that are pervasive in the common errors. These findings provide important clues and practical considerations for improving sentiment analysis models for financial applications.
Answering Legal Questions by Learning Neural Attentive Text Representation
Authors: Phi Manh Kien, Ha-Thanh Nguyen, Ngo Xuan Bach, Vu Tran, Minh Le Nguyen, Tu Minh Phuong
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Articles, Question Answering, Retrieval, valid

Text representation plays a vital role in retrieval-based question answering, especially in the legal domain where documents are usually long and complicated. The better the question and the legal documents are represented, the more accurate they are matched. In this paper, we focus on the task of answering legal questions at the article level. Given a legal question, the goal is to retrieve all the correct and valid legal articles, that can be used as the basic to answer the question. We present a retrieval-based model for the task by learning neural attentive text representation. Our text representation method first leverages convolutional neural networks to extract important information in a question and legal articles. Attention mechanisms are then used to represent the question and articles and select appropriate information to align them in a matching process. Experimental results on an annotated corpus consisting of 5,922 Vietnamese legal questions show that our model outperforms state-of-the-art retrieval-based methods for question answering by large margins in terms of both recall and NDCG.
Automatic Charge Identification from Facts: A Few Sentence-Level Charge Annotations is All You Need
Authors: Shounak Paul, Pawan Goyal, Saptarshi Ghosh
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: All, Multi-Task Learning, Sentence

Automatic Charge Identification (ACI) is the task of identifying the relevant charges given the facts of a situation and the statutory laws that define these charges, and is a crucial aspect of the judicial process. Existing works focus on learning charge-side representations by modeling relationships between the charges, but not much effort has been made in improving fact-side representations. We observe that only a small fraction of sentences in the facts actually indicates the charges. We show that by using a very small subset ({\textless} 3{\%}) of fact descriptions annotated with sentence-level charges, we can achieve an improvement across a range of different ACI models, as compared to modeling just the main document-level task on a much larger dataset. Additionally, we propose a novel model that utilizes sentence-level charge labels as an auxiliary task, coupled with the main task of document-level charge identification in a multi-task learning framework. The proposed model comprehensively outperforms a large number of recent baselines for ACI. The improvement in performance is particularly noticeable for the rare charges which are known to be especially challenging to identify.
Context-Aware Text Normalisation for Historical Dialects
Authors: Maria Sukhareva
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Language Modeling, Language Modelling, Machine Translation, Reranking, Transfer Learning, Translation

Context-aware historical text normalisation is a severely under-researched area. To fill the gap we propose a context-aware normalisation approach that relies on the state-of-the-art methods in neural machine translation and transfer learning. We propose a multidialect normaliser with a context-aware reranking of the candidates. The reranker relies on a word-level n-gram language model that is applied to the five best normalisation candidates. The results are evaluated on the historical multidialect datasets of German, Spanish, Portuguese and Slovene. We show that incorporating dialectal information into the training leads to an accuracy improvement on all the datasets. The context-aware reranking gives further improvement over the baseline. For three out of six datasets, we reach a significantly higher accuracy than reported in the previous studies. The other three results are comparable with the current state-of-the-art. The code for the reranker is published as open-source.
Exploring Amharic Sentiment Analysis from Social Media Texts: Building Annotation Tools and Classification Models
Authors: Seid Muhie Yimam, Hizkiel Mitiku Alemayehu, Abinew Ayele, Chris Biemann
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Decision Making, Sentiment Analysis, text annotation

This paper presents the study of sentiment analysis for Amharic social media texts. As the number of social media users is ever-increasing, social media platforms would like to understand the latent meaning and sentiments of a text to enhance decision-making procedures. However, low-resource languages such as Amharic have received less attention due to several reasons such as lack of well-annotated datasets, unavailability of computing resources, and fewer or no expert researchers in the area. This research addresses three main research questions. We first explore the suitability of existing tools for the sentiment analysis task. Annotation tools are scarce to support large-scale annotation tasks in Amharic. Also, the existing crowdsourcing platforms do not support Amharic text annotation. Hence, we build a social-network-friendly annotation tool called {`}ASAB{'} using the Telegram bot. We collect 9.4k tweets, where each tweet is annotated by three Telegram users. Moreover, we explore the suitability of machine learning approaches for Amharic sentiment analysis. The FLAIR deep learning text classifier, based on network embeddings that are computed from a distributional thesaurus, outperforms other supervised classifiers. We further investigate the challenges in building a sentiment analysis system for Amharic and we found that the widespread usage of sarcasm and figurative speech are the main issues in dealing with the problem. To advance the sentiment analysis research in Amharic and other related low-resource languages, we release the dataset, the annotation tool, source code, and models publicly under a permissive.
Effective Few-Shot Classification with Transfer Learning
Authors: Aakriti Gupta, Kapil Thadani, Neil O{'}Hare
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Classification, Few-Shot Learning, Few-Shot Text Classification, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification, Transfer Learning, zero-shot-classification, Zero-Shot Learning

Few-shot learning addresses the the problem of learning based on a small amount of training data. Although more well-studied in the domain of computer vision, recent work has adapted the Amazon Review Sentiment Classification (ARSC) text dataset for use in the few-shot setting. In this work, we use the ARSC dataset to study a simple application of transfer learning approaches to few-shot classification. We train a single binary classifier to learn all few-shot classes jointly by prefixing class identifiers to the input text. Given the text and class, the model then makes a binary prediction for that text/class pair. Our results show that this simple approach can outperform most published results on this dataset. Surprisingly, we also show that including domain information as part of the task definition only leads to a modest improvement in model accuracy, and zero-shot classification, without further fine-tuning on few-shot domains, performs equivalently to few-shot classification. These results suggest that the classes in the ARSC few-shot task, which are defined by the intersection of domain and rating, are actually very similar to each other, and that a more suitable dataset is needed for the study of few-shot text classification.
SWAFN: Sentimental Words Aware Fusion Network for Multimodal Sentiment Analysis
Authors: Minping Chen, Xia Li
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Multimodal Sentiment Analysis, Sentiment Analysis

Multimodal sentiment analysis aims to predict sentiment of language text with the help of other modalities, such as vision and acoustic features. Previous studies focused on learning the joint representation of multiple modalities, ignoring some useful knowledge contained in language modal. In this paper, we try to incorporate sentimental words knowledge into the fusion network to guide the learning of joint representation of multimodal features. Our method consists of two components: shallow fusion part and aggregation part. For the shallow fusion part, we use crossmodal coattention mechanism to obtain bidirectional context information of each two modals to get the fused shallow representations. For the aggregation part, we design a multitask of sentimental words classification to help and guide the deep fusion of the three modalities and obtain the final sentimental words aware fusion representation. We carry out several experiments on CMU-MOSI, CMU-MOSEI and YouTube datasets. The experimental results show that introducing sentimental words prediction as a multitask can really improve the fusion representation of multiple modalities.
Multimodal Topic-Enriched Auxiliary Learning for Depression Detection
Authors: Minghui An, Jingjing Wang, Shoushan Li, Guodong Zhou
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Auxiliary Learning, Depression Detection

From the perspective of health psychology, human beings with long-term and sustained negativity are highly possible to be diagnosed with depression. Inspired by this, we argue that the global topic information derived from user-generated contents (e.g., texts and images) is crucial to boost the performance of the depression detection task, though this information has been neglected by almost all previous studies on depression detection. To this end, we propose a new Multimodal Topic-enriched Auxiliary Learning (MTAL) approach, aiming at capturing the topic information inside different modalities (i.e., texts and images) for depression detection. Especially, in our approach, a modality-agnostic topic model is proposed to be capable of mining the topical clues from either the discrete textual signals or the continuous visual signals. On this basis, the topic modeling w.r.t. the two modalities are cast as two auxiliary tasks for improving the performance of the primary task (i.e., depression detection). Finally, the detailed evaluation demonstrates the great advantage of our MTAL approach to depression detection over the state-of-the-art baselines. This justifies the importance of the multimodal topic information to depression detection and the effectiveness of our approach in capturing such information.
Meet Changes with Constancy: Learning Invariance in Multi-Source Translation
Authors: Jianfeng Liu, Ling Luo, Xiang Ao, Yan Song, Haoran Xu, Jian Ye
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Machine Translation, NMT, Translation

Multi-source neural machine translation aims to translate from parallel sources of information (e.g. languages, images, etc.) to a single target language, which has shown better performance than most one-to-one systems. Despite the remarkable success of existing models, they usually neglect the fact that multiple source inputs may have inconsistencies. Such differences might bring noise to the task and limit the performance of existing multi-source NMT approaches due to their indiscriminate usage of input sources for target word predictions. In this paper, we attempt to leverage the potential complementary information among distinct sources and alleviate the occasional conflicts of them. To accomplish that, we propose a source invariance network to learn the invariant information of parallel sources. Such network can be easily integrated with multi-encoder based multi-source NMT methods (e.g. multi-encoder RNN and transformer) to enhance the translation results. Extensive experiments on two multi-source translation tasks demonstrate that the proposed approach not only achieves clear gains in translation quality but also captures implicit invariance between different sources.