pwc-archive/papers-with-abstracts huggingface dataset:
Toward Comprehensive Understanding of a Sentiment Based on Human Motives
Authors: Naoki Otani, Eduard Hovy
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Transfer Learning

In sentiment detection, the natural language processing community has focused on determining holders, facets, and valences, but has paid little attention to the reasons for sentiment decisions. Our work considers human motives as the driver for human sentiments and addresses the problem of motive detection as the first step. Following a study in psychology, we define six basic motives that cover a wide range of topics appearing in review texts, annotate 1,600 texts in restaurant and laptop domains with the motives, and report the performance of baseline methods on this new dataset. We also show that cross-domain transfer learning boosts detection performance, which indicates that these universal motives exist across different domains.
Yes, we can! Mining Arguments in 50 Years of US Presidential Campaign Debates
Authors: Shohreh Haddadan, Elena Cabrio, Serena Villata
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Argument Mining

Political debates offer a rare opportunity for citizens to compare the candidates{'} positions on the most controversial topics of the campaign. Thus they represent a natural application scenario for Argument Mining. As existing research lacks solid empirical investigation of the typology of argument components in political debates, we fill this gap by proposing an Argument Mining approach to political debates. We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims. We then propose two tasks: (1) identifying the argumentative components in such debates, and (2) classifying them as premises and claims. We show that feature-rich SVM learners and Neural Network architectures outperform standard baselines in Argument Mining over such complex data. We release the new corpus USElecDeb60To16 and the accompanying software under free licenses to the research community.
An Empirical Study of Span Representations in Argumentation Structure Parsing
Authors: Tatsuki Kuribayashi, Hiroki Ouchi, Naoya Inoue, Paul Reisert, Toshinori Miyoshi, Jun Suzuki, Kentaro Inui
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

For several natural language processing (NLP) tasks, span representation design is attracting considerable attention as a promising new technique; a common basis for an effective design has been established. With such basis, exploring task-dependent extensions for argumentation structure parsing (ASP) becomes an interesting research direction. This study investigates (i) span representation originally developed for other NLP tasks and (ii) a simple task-dependent extension for ASP. Our extensive experiments and analysis show that these representations yield high performance for ASP and provide some challenging types of instances to be parsed.
Neural Network Alignment for Sentential Paraphrases
Authors: Jessica Ouyang, Kathy Mckeown
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: RTE, Sentence

We present a monolingual alignment system for long, sentence- or clause-level alignments, and demonstrate that systems designed for word- or short phrase-based alignment are ill-suited for these longer alignments. Our system is capable of aligning semantically similar spans of arbitrary length. We achieve significantly higher recall on aligning phrases of four or more words and outperform state-of-the- art aligners on the long alignments in the MSR RTE corpus.
Duality of Link Prediction and Entailment Graph Induction
Authors: Mohammad Javad Hosseini, Shay B. Cohen, Mark Johnson, Mark Steedman
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Link Prediction, Prediction

Link prediction and entailment graph induction are often treated as different problems. In this paper, we show that these two problems are actually complementary. We train a link prediction model on a knowledge graph of assertions extracted from raw text. We propose an entailment score that exploits the new facts discovered by the link prediction model, and then form entailment graphs between relations. We further use the learned entailments to predict improved link prediction scores. Our results show that the two tasks can benefit from each other. The new entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction scores.
Detecting Subevents using Discourse and Narrative Features
Authors: Mohammed Aldawsari, Mark Finlayson
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

Recognizing the internal structure of events is a challenging language processing task of great importance for text understanding. We present a supervised model for automatically identifying when one event is a subevent of another. Building on prior work, we introduce several novel features, in particular discourse and narrative features, that significantly improve upon prior state-of-the-art performance. Error analysis further demonstrates the utility of these features. We evaluate our model on the only two annotated corpora with event hierarchies: HiEve and the Intelligence Community corpus. No prior system has been evaluated on both corpora. Our model outperforms previous systems on both corpora, achieving 0.74 BLANC F1 on the Intelligence Community corpus and 0.70 F1 on the HiEve corpus, respectively a 15 and 5 percentage point improvement over previous models.
Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment
Authors: Goran Glava{\v{s}}, Ivan Vuli{\'c}
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Lexical Entailment, Relation, Text Generation

Lexical entailment (LE; also known as hyponymy-hypernymy or is-a relation) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation. In this work, we propose a simple and effective method for fine-tuning distributional word vectors for LE. Our Generalized Lexical ENtailment model (GLEN) is decoupled from the word embedding model and applicable to any distributional vector space. Yet {--} unlike existing retrofitting models {--} it captures a general specialization function allowing for LE-tuning of the entire distributional space and not only the vectors of words seen in lexical constraints. Coupled with a multilingual embedding space, GLEN seamlessly enables cross-lingual LE detection. We demonstrate the effectiveness of GLEN in graded LE and report large improvements (over 20{\%} in accuracy) over state-of-the-art in cross-lingual LE detection.
A Surprisingly Robust Trick for the Winograd Schema Challenge
Authors: Vid Kocijan, Ana-Maria Cretu, Oana-Maria Camburu, Yordan Yordanov, Thomas Lukasiewicz
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Language Modeling, Language Modelling, Natural Language Understanding, WNLI

The Winograd Schema Challenge (WSC) dataset WSC273 and its inference counterpart WNLI are popular benchmarks for natural language understanding and commonsense reasoning. In this paper, we show that the performance of three language models on WSC273 consistently and robustly improves when fine-tuned on a similar pronoun disambiguation problem dataset (denoted WSCR). We additionally generate a large unsupervised WSC-like dataset. By fine-tuning the BERT language model both on the introduced and on the WSCR dataset, we achieve overall accuracies of 72.5{\%} and 74.7{\%} on WSC273 and WNLI, improving the previous state-of-the-art solutions by 8.8{\%} and 9.6{\%}, respectively. Furthermore, our fine-tuned models are also consistently more accurate on the {``}complex{''} subsets of WSC273, introduced by Trichelair et al. (2018).
Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model
Authors: Wei Li, Jingjing Xu, Yancheng He, ShengLi Yan, Yunfang Wu, Xu sun
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Articles, Decoder, Graph-to-Sequence

Automatic article commenting is helpful in encouraging user engagement on online news platforms. However, the news documents are usually too long for models under traditional encoder-decoder frameworks, which often results in general and irrelevant comments. In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. By organizing the article into graph structure, our model can better understand the internal structure of the article and the connection between topics, which makes it better able to generate coherent and informative comments. We collect and release a large scale news-comment corpus from a popular Chinese online news platform Tencent Kuaibao. Extensive experiment results show that our model can generate much more coherent and informative comments compared with several strong baseline models.
Multilingual and Cross-Lingual Graded Lexical Entailment
Authors: Ivan Vuli{\'c}, Simone Paolo Ponzetto, Goran Glava{\v{s}}
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Lexical Entailment

Grounded in cognitive linguistics, graded lexical entailment (GR-LE) is concerned with fine-grained assertions regarding the directional hierarchical relationships between concepts on a continuous scale. In this paper, we present the first work on cross-lingual generalisation of GR-LE relation. Starting from HyperLex, the only available GR-LE dataset in English, we construct new monolingual GR-LE datasets for three other languages, and combine those to create a set of six cross-lingual GR-LE datasets termed CL-HYPERLEX. We next present a novel method dubbed CLEAR (Cross-Lingual Lexical Entailment Attract-Repel) for effectively capturing graded (and binary) LE, both monolingually in different languages as well as across languages (i.e., on CL-HYPERLEX). Coupled with a bilingual dictionary, CLEAR leverages taxonomic LE knowledge in a resource-rich language (e.g., English) and propagates it to other languages. Supported by cross-lingual LE transfer, CLEAR sets competitive baseline performance on three new monolingual GR-LE datasets and six cross-lingual GR-LE datasets. In addition, we show that CLEAR outperforms current state-of-the-art on binary cross-lingual LE detection by a wide margin for diverse language pairs.
Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums
Authors: Zi Chai, Xinyu Xing, Xiaojun Wan, Bo Huang
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Text Generation

Teaching machines to ask questions is an important yet challenging task. Most prior work focused on generating questions with fixed answers. As contents are highly limited by given answers, these questions are often not worth discussing. In this paper, we take the first step on teaching machines to ask open-answered questions from real-world news for open discussion (openQG). To generate high-qualified questions, effective ways for question evaluation are required. We take the perspective that the more answers a question receives, the better it is for open discussion, and analyze how language use affects the number of answers. Compared with other factors, e.g. topic and post time, linguistic factors keep our evaluation from being domain-specific. We carefully perform variable control on 11.5M questions from online forums to get a dataset, OQRanD, and further perform question analysis. Based on these conclusions, several models are built for question evaluation. For openQG task, we construct OQGenD, the first dataset as far as we know, and propose a model based on conditional generative adversarial networks and our question evaluation model. Experiments show that our model can generate questions with higher quality compared with commonly-used text generation methods.
Tree LSTMs with Convolution Units to Predict Stance and Rumor Veracity in Social Media Conversations
Authors: Sumeet Kumar, Kathleen Carley
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Classification, General Classification, Stance Classification, Veracity Classification

Learning from social-media conversations has gained significant attention recently because of its applications in areas like rumor detection. In this research, we propose a new way to represent social-media conversations as binarized constituency trees that allows comparing features in source-posts and their replies effectively. Moreover, we propose to use convolution units in Tree LSTMs that are better at learning patterns in features obtained from the source and reply posts. Our Tree LSTM models employ multi-task (stance + rumor) learning and propagate the useful stance signal up in the tree for rumor classification at the root node. The proposed models achieve state-of-the-art performance, outperforming the current best model by 12{\%} and 15{\%} on F1-macro for rumor-veracity classification and stance classification tasks respectively.
Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization
Authors: Panagiotis Kouris, Alex, Georgios ridis, Andreas Stafylopatis
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Abstractive Text Summarization, Decoder, Form, Text Summarization

This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along with semantic data transformations. Initially, a theoretical model for semantic-based text generalization is introduced and used in conjunction with a deep encoder-decoder architecture in order to produce a summary in generalized form. Subsequently, a methodology is proposed which transforms the aforementioned generalized summary into human-readable form, retaining at the same time important informational aspects of the original text and addressing the problem of out-of-vocabulary or rare words. The overall approach is evaluated on two popular datasets with encouraging results.
Studying Summarization Evaluation Metrics in the Appropriate Scoring Range
Authors: Maxime Peyrard
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

In summarization, automatic evaluation metrics are usually compared based on their ability to correlate with human judgments. Unfortunately, the few existing human judgment datasets have been created as by-products of the manual evaluations performed during the DUC/TAC shared tasks. However, modern systems are typically better than the best systems submitted at the time of these shared tasks. We show that, surprisingly, evaluation metrics which behave similarly on these datasets (average-scoring range) strongly disagree in the higher-scoring range in which current systems now operate. It is problematic because metrics disagree yet we can{'}t decide which one to trust. This is a call for collecting human judgments for high-scoring summaries as this would resolve the debate over which metrics to trust. This would also be greatly beneficial to further improve summarization systems and metrics alike.
Modeling Affirmative and Negated Action Processing in the Brain with Lexical and Compositional Semantic Models
Authors: Vesna Djokic, Jean Maillard, Luana Bulat, Ekaterina Shutova
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Negation, Semantic Composition, Sentence

Recent work shows that distributional semantic models can be used to decode patterns of brain activity associated with individual words and sentence meanings. However, it is yet unclear to what extent such models can be used to study and decode fMRI patterns associated with specific aspects of semantic composition such as the negation function. In this paper, we apply lexical and compositional semantic models to decode fMRI patterns associated with negated and affirmative sentences containing hand-action verbs. Our results show reduced decoding (correlation) of sentences where the verb is in the negated context, as compared to the affirmative one, within brain regions implicated in action-semantic processing. This supports behavioral and brain imaging studies, suggesting that negation involves reduced access to aspects of the affirmative mental representation. The results pave the way for testing alternate semantic models of negation against human semantic processing in the brain.
Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title
Authors: Huimin Xu, Wenting Wang, Xin Mao, Xinyu Jiang, Man Lan
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Attribute, Attribute Value Extraction, NER, TAG

Supplementing product information by extracting attribute values from title is a crucial task in e-Commerce domain. Previous studies treat each attribute only as an entity type and build one set of NER tags (e.g., BIO) for each of them, leading to a scalability issue which unfits to the large sized attribute system in real world e-Commerce. In this work, we propose a novel approach to support value extraction scaling up to thousands of attributes without losing performance: (1) We propose to regard attribute as a query and adopt only one global set of BIO tags for any attributes to reduce the burden of attribute tag or model explosion; (2) We explicitly model the semantic representations for attribute and title, and develop an attention mechanism to capture the interactive semantic relations in-between to enforce our framework to be attribute comprehensive. We conduct extensive experiments in real-life datasets. The results show that our model not only outperforms existing state-of-the-art NER tagging models, but also is robust and generates promising results for up to 8,906 attributes.
Incorporating Linguistic Constraints into Keyphrase Generation
Authors: Jing Zhao, Yuxiang Zhang
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Keyphrase Generation, Multi-Task Learning

Keyphrases, that concisely describe the high-level topics discussed in a document, are very useful for a wide range of natural language processing tasks. Though existing keyphrase generation methods have achieved remarkable performance on this task, they generate many overlapping phrases (including sub-phrases or super-phrases) of keyphrases. In this paper, we propose the parallel Seq2Seq network with the coverage attention to alleviate the overlapping phrase problem. Specifically, we integrate the linguistic constraints of keyphrase into the basic Seq2Seq network on the source side, and employ the multi-task learning framework on the target side. In addition, in order to prevent from generating overlapping phrases of keyphrases with correct syntax, we introduce the coverage vector to keep track of the attention history and to decide whether the parts of source text have been covered by existing generated keyphrases. Experimental results show that our method can outperform the state-of-the-art CopyRNN on scientific datasets, and is also more effective in news domain.
A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining
Authors: Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Multi-Task Learning, Pharmacovigilance

The mining of adverse drug reaction (ADR) has a crucial role in the pharmacovigilance. The traditional ways of identifying ADR are reliable but time-consuming, non-scalable and offer a very limited amount of ADR relevant information. With the unprecedented growth of information sources in the forms of social media texts (Twitter, Blogs, Reviews etc.), biomedical literature, and Electronic Medical Records (EMR), it has become crucial to extract the most pertinent ADR related information from these free-form texts. In this paper, we propose a neural network inspired multi- task learning framework that can simultaneously extract ADRs from various sources. We adopt a novel adversarial learning-based approach to learn features across multiple ADR information sources. Unlike the other existing techniques, our approach is capable to extracting fine-grained information (such as {`}Indications{'}, {`}Symptoms{'}, {`}Finding{'}, {`}Disease{'}, {`}Drug{'}) which provide important cues in pharmacovigilance. We evaluate our proposed approach on three publicly available real- world benchmark pharmacovigilance datasets, a Twitter dataset from PSB 2016 Social Me- dia Shared Task, CADEC corpus and Medline ADR corpus. Experiments show that our unified framework achieves state-of-the-art performance on individual tasks associated with the different benchmark datasets. This establishes the fact that our proposed approach is generic, which enables it to achieve high performance on the diverse datasets.
A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification
Authors: Pengcheng Yang, Fuli Luo, Shuming Ma, Junyang Lin, Xu sun
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Reinforcement Learning, Sensitivity

Multi-label classification (MLC) aims to predict a set of labels for a given instance. Based on a pre-defined label order, the sequence-to-sequence (Seq2Seq) model trained via maximum likelihood estimation method has been successfully applied to the MLC task and shows powerful ability to capture high-order correlations between labels. However, the output labels are essentially an unordered set rather than an ordered sequence. This inconsistency tends to result in some intractable problems, e.g., sensitivity to the label order. To remedy this, we propose a simple but effective sequence-to-set model. The proposed model is trained via reinforcement learning, where reward feedback is designed to be independent of the label order. In this way, we can reduce the dependence of the model on the label order, as well as capture high-order correlations between labels. Extensive experiments show that our approach can substantially outperform competitive baselines, as well as effectively reduce the sensitivity to the label order.
Exploring Pre-trained Language Models for Event Extraction and Generation
Authors: Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, Dongsheng Li
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Event Extraction, General Classification

Traditional approaches to the task of ACE event extraction usually depend on manually annotated data, which is often laborious to create and limited in size. Therefore, in addition to the difficulty of event extraction itself, insufficient training data hinders the learning process as well. To promote event extraction, we first propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles. Moreover, to address the problem of insufficient training data, we propose a method to automatically generate labeled data by editing prototypes and screen out generated samples by ranking the quality. Experiments on the ACE2005 dataset demonstrate that our extraction model can surpass most existing extraction methods. Besides, incorporating our generation method exhibits further significant improvement. It obtains new state-of-the-art results on the event extraction task, including pushing the F1 score of trigger classification to 81.1{\%}, and the F1 score of argument classification to 58.9{\%}.