pwc-archive/papers-with-abstracts huggingface dataset:
Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering
Authors: Zhiguo Wang, Patrick Ng, Ramesh Nallapati, Bing Xiang
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Entity Linking, Information Retrieval, Knowledge Base Question Answering, Multi-Task Learning, Question Answering, Re-Ranking, Retrieval

Question answering over knowledge bases (KBQA) usually involves three sub-tasks, namely topic entity detection, entity linking and relation detection. Due to the large number of entities and relations inside knowledge bases (KB), previous work usually utilized sophisticated rules to narrow down the search space and managed only a subset of KBs in memory. In this work, we leverage a \textit{retrieve-and-rerank} framework to access KBs via traditional information retrieval (IR) method, and re-rank retrieved candidates with more powerful neural networks such as the pre-trained BERT model. Considering the fact that directly assigning a different BERT model for each sub-task may incur prohibitive costs, we propose to share a BERT encoder across all three sub-tasks and define task-specific layers on top of the shared layer. The unified model is then trained under a multi-task learning framework. Experiments show that: (1) Our IR-based retrieval method is able to collect high-quality candidates efficiently, thus enables our method adapt to large-scale KBs easily; (2) the BERT model improves the accuracy across all three sub-tasks; and (3) benefiting from multi-task learning, the unified model obtains further improvements with only 1/3 of the original parameters. Our final model achieves competitive results on the SimpleQuestions dataset and superior performance on the FreebaseQA dataset.
Implicitly Abusive Comparisons -- A New Dataset and Linguistic Analysis
Authors: Michael Wiegand, Maja Geulig, Josef Ruppenhofer
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

We examine the task of detecting implicitly abusive comparisons (e.g. {``}Your hair looks like you have been electrocuted{''}). Implicitly abusive comparisons are abusive comparisons in which abusive words (e.g. {``}dumbass{''} or {``}scum{''}) are absent. We detail the process of creating a novel dataset for this task via crowdsourcing that includes several measures to obtain a sufficiently representative and unbiased set of comparisons. We also present classification experiments that include a range of linguistic features that help us better understand the mechanisms underlying abusive comparisons.
Exploiting Emojis for Abusive Language Detection
Authors: Michael Wiegand, Josef Ruppenhofer
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Abusive Language, domain classification

We propose to use abusive emojis, such as the {``}middle finger{''} or {``}face vomiting{''}, as a proxy for learning a lexicon of abusive words. Since it represents extralinguistic information, a single emoji can co-occur with different forms of explicitly abusive utterances. We show that our approach generates a lexicon that offers the same performance in cross-domain classification of abusive microposts as the most advanced lexicon induction method. Such an approach, in contrast, is dependent on manually annotated seed words and expensive lexical resources for bootstrapping (e.g. WordNet). We demonstrate that the same emojis can also be effectively used in languages other than English. Finally, we also show that emojis can be exploited for classifying mentions of ambiguous words, such as {``}fuck{''} and {``}bitch{''}, into generally abusive and just profane usages.
Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks
Authors: Nisansa de Silva, Dejing Dou
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Semantic Similarity, Semantic Textual Similarity

Social networks face a major challenge in the form of rumors and fake news, due to their intrinsic nature of connecting users to millions of others, and of giving any individual the power to post anything. Given the rapid, widespread dissemination of information in social networks, manually detecting suspicious news is sub-optimal. Thus, research on automatic rumor detection has become a necessity. Previous works in the domain have utilized the reply relations between posts, as well as the semantic similarity between the main post and its context, consisting of replies, in order to obtain state-of-the-art performance. In this work, we demonstrate that semantic oppositeness can improve the performance on the task of rumor detection. We show that semantic oppositeness captures elements of discord, which are not properly covered by previous efforts, which only utilize semantic similarity or reply structure. We show, with extensive experiments on recent data sets for this problem, that our proposed model achieves state-of-the-art performance. Further, we show that our model is more resistant to the variances in performance introduced by randomness.
Knowledge Base Question Answering through Recursive Hypergraphs
Authors: Naganand Yadati, Dayanidhi R S, Vaishnavi S, Indira K M, Srinidhi G
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Knowledge Base Question Answering, Question Answering

Knowledge Base Question Answering (KBQA) is the problem of predicting an answer for a factoid question over a given knowledge base (KB). Answering questions typically requires reasoning over multiple links in the given KB. Humans tend to answer questions by grouping different objects to perform reasoning over acquired knowledge. Hypergraphs provide a natural tool to model group relationships. In this work, inspired by typical human intelligence, we propose a new method for KBQA based on hypergraphs. Existing methods for KBQA, though effective, do not explicitly incorporate the recursive relational group structure in the given KB. Our method, which we name RecHyperNet (Recursive Hypergraph Network), exploits a new way of modelling KBs through recursive hypergraphs to organise such group relationships in KBs. Experiments on multiple KBQA benchmarks demonstrate the effectiveness of the proposed RecHyperNet. We have released the code.
Grounding as a Collaborative Process
Authors: Luciana Benotti, Patrick Blackburn
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Language Acquisition

Collaborative grounding is a fundamental aspect of human-human dialog which allows people to negotiate meaning. In this paper we argue that it is missing from current deep learning approaches to dialog. Our central point is that making mistakes and being able to recover from them collaboratively is a key ingredient in grounding meaning. We illustrate the pitfalls of being unable to ground collaboratively, discuss what can be learned from the language acquisition and dialog systems literature, and reflect on how to move forward.
Does She Wink or Does She Nod? A Challenging Benchmark for Evaluating Word Understanding of Language Models
Authors: Lutfi Kerem Senel, Hinrich Sch{\"u}tze
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Language Modeling, Language Modelling

Recent progress in pretraining language models on large corpora has resulted in significant performance gains on many NLP tasks. These large models acquire linguistic knowledge during pretraining, which helps to improve performance on downstream tasks via fine-tuning. To assess what kind of knowledge is acquired, language models are commonly probed by querying them with {`}fill in the blank{'} style cloze questions. Existing probing datasets mainly focus on knowledge about relations between words and entities. We introduce WDLMPro (Word Definitions Language Model Probing) to evaluate word understanding directly using dictionary definitions of words. In our experiments, three popular pretrained language models struggle to match words and their definitions. This indicates that they understand many words poorly and that our new probing task is a difficult challenge that could help guide research on LMs in the future.
Predicting Treatment Outcome from Patient Texts:The Case of Internet-Based Cognitive Behavioural Therapy
Authors: Evangelia Gogoulou, Magnus Boman, Fehmi ben Abdesslem, Nils Hentati Isacsson, Viktor Kaldo, Magnus Sahlgren
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Sentiment Analysis

We investigate the feasibility of applying standard text categorisation methods to patient text in order to predict treatment outcome in Internet-based cognitive behavioural therapy. The data set is unique in its detail and size for regular care for depression, social anxiety, and panic disorder. Our results indicate that there is a signal in the depression data, albeit a weak one. We also perform terminological and sentiment analysis, which confirm those results.
Is ``hot pizza'' Positive or Negative? Mining Target-aware Sentiment Lexicons
Authors: Jie zhou, Yuanbin Wu, Changzhi Sun, Liang He
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Relation Extraction, Sentiment Analysis

Modelling a word{'}s polarity in different contexts is a key task in sentiment analysis. Previous works mainly focus on domain dependencies, and assume words{'} sentiments are invariant within a specific domain. In this paper, we relax this assumption by binding a word{'}s sentiment to its collocation words instead of domain labels. This finer view of sentiment contexts is particularly useful for identifying commonsense sentiments expressed in neural words such as {``}big{''} and {``}long{''}. Given a target (e.g., an aspect), we propose an effective {``}perturb-and-see{''} method to extract sentiment words modifying it from large-scale datasets. The reliability of the obtained target-aware sentiment lexicons is extensively evaluated both manually and automatically. We also show that a simple application of the lexicon is able to achieve highly competitive performances on the unsupervised opinion relation extraction task.
How Fast can BERT Learn Simple Natural Language Inference?
Authors: Yi-Chung Lin, Keh-Yih Su
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Natural Language Inference

This paper empirically studies whether BERT can really learn to conduct natural language inference (NLI) without utilizing hidden dataset bias; and how efficiently it can learn if it could. This is done via creating a simple entailment judgment case which involves only binary predicates in plain English. The results show that the learning process of BERT is very slow. However, the efficiency of learning can be greatly improved (data reduction by a factor of 1,500) if task-related features are added. This suggests that domain knowledge greatly helps when conducting NLI with neural networks.
Query Generation for Multimodal Documents
Authors: Kyungho Kim, Kyungjae Lee, Seung-won Hwang, Young-In Song, SeungWook Lee
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Reranking, Retrieval

This paper studies the problem of generatinglikely queries for multimodal documents withimages. Our application scenario is enablingefficient {``}first-stage retrieval{''} of relevant doc-uments, by attaching generated queries to doc-uments before indexing. We can then indexthis expanded text to efficiently narrow downto candidate matches using inverted index, sothat expensive reranking can follow. Our eval-uation results show that our proposed multi-modal representation meaningfully improvesrelevance ranking.More importantly, ourframework can achieve the state of the art inthe first stage retrieval scenarios
End-to-End Argument Mining as Biaffine Dependency Parsing
Authors: Yuxiao Ye, Simone Teufel
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Argument Mining, Dependency Parsing, Feature Engineering, Relation, Relation Extraction

Non-neural approaches to argument mining (AM) are often pipelined and require heavy feature-engineering. In this paper, we propose a neural end-to-end approach to AM which is based on dependency parsing, in contrast to the current state-of-the-art which relies on relation extraction. Our biaffine AM dependency parser significantly outperforms the state-of-the-art, performing at F1 = 73.5{\%} for component identification and F1 = 46.4{\%} for relation identification. One of the advantages of treating AM as biaffine dependency parsing is the simple neural architecture that results. The idea of treating AM as dependency parsing is not new, but has previously been abandoned as it was lagging far behind the state-of-the-art. In a thorough analysis, we investigate the factors that contribute to the success of our model: the biaffine model itself, our representation for the dependency structure of arguments, different encoders in the biaffine model, and syntactic information additionally fed to the model. Our work demonstrates that dependency parsing for AM, an overlooked idea from the past, deserves more attention in the future.
Dialogue Act-based Breakdown Detection in Negotiation Dialogues
Authors: Atsuki Yamaguchi, Kosui Iwasa, Katsuhide Fujita
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

Thanks to the success of goal-oriented negotiation dialogue systems, studies of negotiation dialogue have gained momentum in terms of both human-human negotiation support and dialogue systems. However, the field suffers from a paucity of available negotiation corpora, which hinders further development and makes it difficult to test new methodologies in novel negotiation settings. Here, we share a human-human negotiation dialogue dataset in a job interview scenario that features increased complexities in terms of the number of possible solutions and a utility function. We test the proposed corpus using a breakdown detection task for human-human negotiation support. We also introduce a dialogue act-based breakdown detection method, focusing on dialogue flow that is applicable to various corpora. Our results show that our proposed method features comparable detection performance to text-based approaches in existing corpora and better results in the proposed dataset.
Multiple Tasks Integration: Tagging, Syntactic and Semantic Parsing as a Single Task
Authors: Timoth{\'e}e Bernard
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Dependency Parsing, Part-Of-Speech Tagging, Reinforcement Learning (RL), Semantic Dependency Parsing, Semantic Parsing

Departing from both sequential pipelines and monotask systems, we propose Multiple Tasks Integration (MTI), a multitask paradigm orthogonal to weight sharing. The essence of MTI is to process the input iteratively but concurrently at multiple levels of analysis, where each decision is based on all of the structures that are already inferred and free from usual ordering constraints. We illustrate MTI with a system that performs part-of-speech tagging, syntactic dependency parsing and semantic dependency parsing. We observe that both the use of reinforcement learning and the release from sequential constraints are beneficial to the quality of the syntactic and semantic parses. We also observe that our model adopts an easy-first strategy that consists, on average, of predicting shorter dependencies before longer ones, but that syntax is not always tackled before semantics.
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set
Authors: Anca Tache, Gaman Mihaela, Radu Tudor Ionescu
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Clustering, Sentiment Analysis, Sentiment Classification, Text Categorization, Word Embeddings

Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools. In this paper, we introduce LaRoSeDa, a Large Romanian Sentiment Data Set, which is composed of 15,000 positive and negative reviews collected from the largest Romanian e-commerce platform. We employ two sentiment classification methods as baselines for our new data set, one based on low-level features (character n-grams) and one based on high-level features (bag-of-word-embeddings generated by clustering word embeddings with k-means). As an additional contribution, we replace the k-means clustering algorithm with self-organizing maps (SOMs), obtaining better results because the generated clusters of word embeddings are closer to the Zipf{'}s law distribution, which is known to govern natural language. We also demonstrate the generalization capacity of using SOMs for the clustering of word embeddings on another recently-introduced Romanian data set, for text categorization by topic.
Identifying Named Entities as they are Typed
Authors: Ravneet Arora, Chen-Tse Tsai, Daniel Preotiuc-Pietro
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence

Identifying named entities in written text is an essential component of the text processing pipeline used in applications such as text editors to gain a better understanding of the semantics of the text. However, the typical experimental setup for evaluating Named Entity Recognition (NER) systems is not directly applicable to systems that process text in real time as the text is being typed. Evaluation is performed on a sentence level assuming the end-user is willing to wait until the entire sentence is typed for entities to be identified and further linked to identifiers or co-referenced. We introduce a novel experimental setup for NER systems for applications where decisions about named entity boundaries need to be performed in an online fashion. We study how state-of-the-art methods perform under this setup in multiple languages and propose adaptations to these models to suit this new experimental setup. Experimental results show that the best systems that are evaluated on each token after its typed, reach performance within 1{--}5 F1 points of systems that are evaluated at the end of the sentence. These show that entity recognition can be performed in this setup and open up the development of other NLP tools in a similar setup.
SANDI: Story-and-Images Alignment
Authors: Sreyasi Nag Chowdhury, Simon Razniewski, Gerhard Weikum
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

The Internet contains a multitude of social media posts and other of stories where text is interspersed with images. In these contexts, images are not simply used for general illustration, but are judiciously placed in certain spots of a story for multimodal descriptions and narration. In this work we analyze the problem of text-image alignment, and present SANDI, a methodology for automatically selecting images from an image collection and aligning them with text paragraphs of a story. SANDI combines visual tags, user-provided tags and background knowledge, and uses an Integer Linear Program to compute alignments that are semantically meaningful. Experiments show that SANDI can select and align images with texts with high quality of semantic fit.
Discourse-Aware Unsupervised Summarization for Long Scientific Documents
Authors: Yue Dong, Andrei Mircea, Jackie Chi Kit Cheung
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Articles, Extractive Summarization, Sentence

We propose an unsupervised graph-based ranking model for extractive summarization of long scientific documents. Our method assumes a two-level hierarchical graph representation of the source document, and exploits asymmetrical positional cues to determine sentence importance. Results on the PubMed and arXiv datasets show that our approach outperforms strong unsupervised baselines by wide margins in automatic metrics and human evaluation. In addition, it achieves performance comparable to many state-of-the-art supervised approaches which are trained on hundreds of thousands of examples. These results suggest that patterns in the discourse structure are a strong signal for determining importance in scientific articles.
MIDAS: A Dialog Act Annotation Scheme for Open Domain HumanMachine Spoken Conversations
Authors: Dian Yu, Zhou Yu
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Transfer Learning

Dialog act prediction in open-domain conversations is an essential language comprehension task for both dialog system building and discourse analysis. Previous dialog act schemes, such as SWBD-DAMSL, are designed mainly for discourse analysis in human-human conversations. In this paper, we present a dialog act annotation scheme, MIDAS (Machine Interaction Dialog Act Scheme), targeted at open-domain human-machine conversations. MIDAS is designed to assist machines to improve their ability to understand human partners. MIDAS has a hierarchical structure and supports multi-label annotations. We collected and annotated a large open-domain human-machine spoken conversation dataset (consisting of 24K utterances). To validate our scheme, we leveraged transfer learning methods to train a multi-label dialog act prediction model and reached an F1 score of 0.79.
Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models
Authors: Tianxing He, Jun Liu, Kyunghyun Cho, Myle Ott, Bing Liu, James Glass, Fuchun Peng
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Decoder, Response Generation, Text Generation, Transfer Learning

In this work, we study how the finetuning stage in the pretrain-finetune framework changes the behavior of a pretrained neural language generator. We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task. Our major finding is that after standard finetuning, the model forgets some of the important language generation skills acquired during large-scale pretraining. We demonstrate the forgetting phenomenon through a set of detailed behavior analysis from the perspectives of knowledge transfer, context sensitivity, and function space projection. As a preliminary attempt to alleviate the forgetting problem, we propose an intuitive finetuning strategy named {``}mix-review{''}. We find that mix-review effectively regularizes the finetuning process, and the forgetting problem is alleviated to some extent. Finally, we discuss interesting behavior of the resulting dialogue model and its implications.
Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yol\'oxochitl Mixtec
Authors: Jiatong Shi, Jonathan D. Amith, Rey Castillo Garc{\'\i}a, Esteban Guadalupe Sierra, Kevin Duh, Shinji Watanabe
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

{``}Transcription bottlenecks{''}, created by a shortage of effective human transcribers (i.e., transcriber shortage), are one of the main challenges to endangered language (EL) documentation. Automatic speech recognition (ASR) has been suggested as a tool to overcome such bottlenecks. Following this suggestion, we investigated the effectiveness for EL documentation of end-to-end ASR, which unlike Hidden Markov Model ASR systems, eschews linguistic resources but is instead more dependent on large-data settings. We open source a Yolox{\'o}chitl Mixtec EL corpus. First, we review our method in building an end-to-end ASR system in a way that would be reproducible by the ASR community. We then propose a novice transcription correction task and demonstrate how ASR systems and novice transcribers can work together to improve EL documentation. We believe this combinatory methodology would mitigate the transcription bottleneck and transcriber shortage that hinders EL documentation.