mtybilly/Instruct2DS huggingface dataset:
Neural News Recommendation with Long- and Short-term User Representations
Authors: Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie Mingxiao An
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Personalized news recommendation is important to help users find their interested news and improve reading experience. A key problem in news recommendation is learning accurate user representations to capture their interests. Users usually have both long-term preferences and short-term interests. However, existing news recommendation methods usually learn single representations of users, which may be insufficient. In this paper, we propose a neural news recommendation approach which can learn both long- and short-term user representations. The core of our approach is a news encoder and a user encoder. In the news encoder, we learn representations of news from their titles and topic categories, and use attention network to select important words. In the user encoder, we propose to learn long-term user representations from the embeddings of their IDs.In addition, we propose to learn short-term user representations from their recently browsed news via GRU network. Besides, we propose two methods to combine long-term and short-term user representations. The first one is using the long-term user representation to initialize the hidden state of the GRU network in short-term user representation. The second one is concatenating both long- and short-term user representations as a unified user vector. Extensive experiments on a real-world dataset show our approach can effectively improve the performance of neural news recommendation.
Automatic Domain Adaptation Outperforms Manual Domain Adaptation for Predicting Financial Outcomes
Authors: Nikolas Breitkopf, Hinrich Schütze Marina Sedinkina
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: In this paper, we automatically create sentiment dictionaries for predicting financial outcomes. We compare three approaches: (i) manual adaptation of the domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first manual, then automatic adaptation. In our experiments, we demonstrate that the automatically adapted sentiment dictionary outperforms the previous state of the art in predicting the financial outcomes excess return and volatility. In particular, automatic adaptation performs better than manual adaptation. In our analysis, we find that annotation based on an expert`s a priori belief about a word`s meaning can be incorrect -- annotation should be performed based on the word`s contexts in the target domain instead.
Manipulating the Difficulty of C-Tests
Authors: Erik Schwan, Christian M. Meyer Ji-Ung Lee
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We propose two novel manipulation strategies for increasing and decreasing the difficulty of C-tests automatically. This is a crucial step towards generating learner-adaptive exercises for self-directed language learning and preparing language assessment tests. To reach the desired difficulty level, we manipulate the size and the distribution of gaps based on absolute and relative gap difficulty predictions. We evaluate our approach in corpus-based experiments and in a user study with 60 participants. We find that both strategies are able to generate C-tests with the desired difficulty level.
Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings
Authors: Adrien Sieg, Léa A. Deleris Zied Haj-Yahia
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Text classification aims at mapping documents into a set of predefined categories. Supervised machine learning models have shown great success in this area but they require a large number of labeled documents to reach adequate accuracy. This is particularly true when the number of target categories is in the tens or the hundreds. In this work, we explore an unsupervised approach to classify documents into categories simply described by a label. The proposed method is inspired by the way a human proceeds in this situation: It draws on textual similarity between the most relevant words in each document and a dictionary of keywords for each category reflecting its semantics and lexical field. The novelty of our method hinges on the enrichment of the category labels through a combination of human expertise and language models, both generic and domain specific. Our experiments on 5 standard corpora show that the proposed method increases F1-score over relying solely on human expertise and can also be on par with simple supervised approaches. It thus provides a practical alternative to situations where low cost text categorization is needed, as we illustrate with our application to operational risk incidents classification.
Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table
Authors: Raheel Nawaz Matthew Shardlow
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Clinical letters are infamously impenetrable for the lay patient. This work uses neural text simplification methods to automatically improve the understandability of clinical letters for patients. We take existing neural text simplification software and augment it with a new phrase table that links complex medical terminology to simpler vocabulary by mining SNOMED-CT. In an evaluation task using crowdsourcing, we show that the results of our new system are ranked easier to understand (average rank 1.93) than using the original system (2.34) without our phrase table. We also show improvement against baselines including the original text (2.79) and using the phrase table without the neural text simplification software (2.94). Our methods can easily be transferred outside of the clinical domain by using domain-appropriate resources to provide effective neural text simplification for any domain without the need for costly annotation.
What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues
Authors: Yi Yang Yu Qin
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Predicting financial risk is an essential task in financial market. Prior research has shown that textual information in a firm`s financial statement can be used to predict its stock`s risk level. Nowadays, firm CEOs communicate information not only verbally through press releases and financial reports, but also nonverbally through investor meetings and earnings conference calls. There are anecdotal evidences that CEO`s vocal features, such as emotions and voice tones, can reveal the firm`s performance. However, how vocal features can be used to predict risk levels, and to what extent, is still unknown. To fill the gap, we obtain earnings call audio recordings and textual transcripts for S&P 500 companies in recent years. We propose a multimodal deep regression model (MDRM) that jointly model CEO`s verbal (from text) and vocal (from audio) information in a conference call. Empirical results show that our model that jointly considers verbal and vocal features achieves significant and substantial prediction error reduction. We also discuss several interesting findings and the implications to financial markets. The processed earnings conference calls data (text and audio) are released for readers who are interested in reproducing the results or designing trading strategy.
Detecting Concealed Information in Text and Speech
Authors: Shengli Hu
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Motivated by infamous cheating scandals in the media industry, the wine industry, and political campaigns, we address the problem of detecting concealed information in technical settings. In this work, we explore acoustic-prosodic and linguistic indicators of information concealment by collecting a unique corpus of professionals practicing for oral exams while concealing information. We reveal subtle signs of concealing information in speech and text, compare and contrast them with those in deception detection literature, uncovering the link between concealing information and deception. We then present a series of experiments that automatically detect concealed information from text and speech. We compare the use of acoustic-prosodic, linguistic, and individual feature sets, using different machine learning models. Finally, we present a multi-task learning framework with acoustic, linguistic, and individual features, that outperforms human performance by over 15%.
Evidence-based Trustworthiness
Authors: Zachary Ives, Dan Roth Yi Zhang
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: The information revolution brought with it information pollution. Information retrieval and extraction help us cope with abundant information from diverse sources. But some sources are of anonymous authorship, and some are of uncertain accuracy, so how can we determine what we should actually believe? Not all information sources are equally trustworthy, and simply accepting the majority view is often wrong. This paper develops a general framework for estimating the trustworthiness of information sources in an environment where multiple sources provide claims and supporting evidence, and each claim can potentially be produced by multiple sources. We consider two settings: one in which information sources directly assert claims, and a more realistic and challenging one, in which claims are inferred from evidence provided by sources, via (possibly noisy) NLP techniques. Our key contribution is to develop a family of probabilistic models that jointly estimate the trustworthiness of sources, and the credibility of claims they assert. This is done while accounting for the (possibly noisy) NLP needed to infer claims from evidence supplied by sources. We evaluate our framework on several datasets, showing strong results and significant improvement over baselines.
Disentangled Representation Learning for Non-Parallel Text Style Transfer
Authors: Lili Mou, Hareesh Bahuleyan, Olga Vechtomova Vineet John
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: This paper tackles the problem of disentangling the latent representations of style and content in language models. We propose a simple yet effective approach, which incorporates auxiliary multi-task and adversarial objectives, for style prediction and bag-of-words prediction, respectively. We show, both qualitatively and quantitatively, that the style and content are indeed disentangled in the latent space. This disentangled latent representation learning can be applied to style transfer on non-parallel corpora. We achieve high performance in terms of transfer accuracy, content preservation, and language fluency, in comparison to various previous approaches.
Cross-Sentence Grammatical Error Correction
Authors: Weiqi Wang, Hwee Tou Ng Shamil Chollampatt
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Automatic grammatical error correction (GEC) research has made remarkable progress in the past decade. However, all existing approaches to GEC correct errors by considering a single sentence alone and ignoring crucial cross-sentence context. Some errors can only be corrected reliably using cross-sentence context and models can also benefit from the additional contextual information in correcting other errors. In this paper, we address this serious limitation of existing approaches and improve strong neural encoder-decoder models by appropriately modeling wider contexts. We employ an auxiliary encoder that encodes previous sentences and incorporate the encoding in the decoder via attention and gating mechanisms. Our approach results in statistically significant improvements in overall GEC performance over strong baselines across multiple test sets. Analysis of our cross-sentence GEC model on a synthetic dataset shows high performance in verb tense corrections that require cross-sentence context.
This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation
Authors: Joel Tetreault Rui Zhang
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Given the overwhelming number of emails, an effective subject line becomes essential to better inform the recipient of the email`s content. In this paper, we propose and study the task of \textitemail subject line generation: automatically generating an email subject line from the email body. We create the first dataset for this task and find that email subject line generation favor extremely abstractive summary which differentiates it from news headline generation or news single document summarization. We then develop a novel deep learning method and compare it to several baselines as well as recent state-of-the-art text summarization systems. We also investigate the efficacy of several automatic metrics based on correlations with human judgments and propose a new automatic evaluation metric. Our system outperforms competitive baselines given both automatic and human evaluations. To our knowledge, this is the first work to tackle the problem of effective email subject line generation.
Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change
Authors: Simon Hengchen, Nina Tahmasebi, Dominik Schlechtweg Haim Dubossarsky
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.
Adversarial Attention Modeling for Multi-dimensional Emotion Regression
Authors: Shoushan Li, Guodong Zhou Suyang Zhu
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: In this paper, we propose a neural network-based approach, namely Adversarial Attention Network, to the task of multi-dimensional emotion regression, which automatically rates multiple emotion dimension scores for an input text. Especially, to determine which words are valuable for a particular emotion dimension, an attention layer is trained to weight the words in an input sequence. Furthermore, adversarial training is employed between two attention layers to learn better word weights via a discriminator. In particular, a shared attention layer is incorporated to learn public word weights between two emotion dimensions. Empirical evaluation on the EMOBANK corpus shows that our approach achieves notable improvements in r-values on both EMOBANK Reader`s and Writer`s multi-dimensional emotion regression tasks in all domains over the state-of-the-art baselines.
Divide, Conquer and Combine: Hierarchical Feature Fusion Network with Local and Global Perspectives for Multimodal Affective Computing
Authors: Haifeng Hu, Songlong Xing Sijie Mai
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We propose a general strategy named \textquoteleftdivide, conquer and combine' for multimodal fusion. Instead of directly fusing features at holistic level, we conduct fusion hierarchically so that both local and global interactions are considered for a comprehensive interpretation of multimodal embeddings. In the \textquoteleftdivide' and \textquoteleftconquer' stages, we conduct local fusion by exploring the interaction of a portion of the aligned feature vectors across various modalities lying within a sliding window, which ensures that each part of multimodal embeddings are explored sufficiently. On its basis, global fusion is conducted in the \textquoteleftcombine' stage to explore the interconnection across local interactions, via an Attentive Bi-directional Skip-connected LSTM that directly connects distant local interactions and integrates two levels of attention mechanism. In this way, local interactions can exchange information sufficiently and thus obtain an overall view of multimodal information. Our method achieves state-of-the-art performance on multimodal affective computing with higher efficiency.
Modeling Financial Analysts' Decision Making via the Pragmatics and Semantics of Earnings Calls
Authors: Amanda Stent Katherine Keith
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts. After these calls, analysts often change their price target recommendations, which are used in equity re- search reports to help investors make deci- sions. In this paper, we examine analysts' decision making behavior as it pertains to the language content of earnings calls. We identify a set of 20 pragmatic features of analysts' questions which we correlate with analysts' pre-call investor recommendations. We also analyze the degree to which semantic and pragmatic features from an earnings call complement market data in predicting analysts' post-call changes in price targets. Our results show that earnings calls are moderately predictive of analysts' decisions even though these decisions are influenced by a number of other factors including private communication with company executives and market conditions. A breakdown of model errors indicates disparate performance on calls from different market sectors.
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis
Authors: Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier Ruidan He
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence. This task is usually done in a pipeline manner, with aspect term extraction performed first, followed by sentiment predictions toward the extracted aspect terms. While easier to develop, such an approach does not fully exploit joint information from the two subtasks and does not use all available sources of training information that might be helpful, such as document-level labeled sentiment corpus. In this paper, we propose an interactive multi-task learning network (IMN) which is able to jointly learn multiple related tasks simultaneously at both the token level as well as the document level. Unlike conventional multi-task learning methods that rely on learning common features for the different tasks, IMN introduces a message passing architecture where information is iteratively passed to different tasks through a shared set of latent variables. Experimental results demonstrate superior performance of the proposed method against multiple baselines on three benchmark datasets.
Decompositional Argument Mining: A General Purpose Approach for Argument Graph Construction
Authors: Chris Reed Debela Gemechu
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: This work presents an approach decomposing propositions into four functional components and identify the patterns linking those components to determine argument structure. The entities addressed by a proposition are target concepts and the features selected to make a point about the target concepts are aspects. A line of reasoning is followed by providing evidence for the points made about the target concepts via aspects. Opinions on target concepts and opinions on aspects are used to support or attack the ideas expressed by target concepts and aspects. The relations between aspects, target concepts, opinions on target concepts and aspects are used to infer the argument relations. Propositions are connected iteratively to form a graph structure. The approach is generic in that it is not tuned for a specific corpus and evaluated on three different corpora from the literature: AAEC, AMT, US2016G1tv and achieved an F score of 0.79, 0.77 and 0.64, respectively.
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Authors: Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, Rada Mihalcea Soujanya Poria
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at r̆lhttp://affective-meld.github.io.
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification
Authors: Yuxing Peng, Zhen Huang, Dongsheng Li, Yiwei Lv Minghao Hu
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence. Prior work typically formulates this task as a sequence tagging problem. However, such formulation suffers from problems such as huge search space and sentiment inconsistency. To address these problems, we propose a span-based extract-then-classify framework, where multiple opinion targets are directly extracted from the sentence under the supervision of target span boundaries, and corresponding polarities are then classified using their span representations. We further investigate three approaches under this framework, namely the pipeline, joint, and collapsed models. Experiments on three benchmark datasets show that our approach consistently outperforms the sequence tagging baseline. Moreover, we find that the pipeline model achieves the best performance compared with the other two models.
Transfer Capsule Network for Aspect Level Sentiment Classification
Authors: Tieyun Qian Zhuang Chen
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Aspect-level sentiment classification aims to determine the sentiment polarity of a sentence towards an aspect. Due to the high cost in annotation, the lack of aspect-level labeled data becomes a major obstacle in this area. On the other hand, document-level labeled data like reviews are easily accessible from online websites. These reviews encode sentiment knowledge in abundant contexts. In this paper, we propose a Transfer Capsule Network (TransCap) model for transferring document-level knowledge to aspect-level sentiment classification. To this end, we first develop an aspect routing approach to encapsulate the sentence-level semantic representations into semantic capsules from both the aspect-level and document-level data. We then extend the dynamic routing approach to adaptively couple the semantic capsules with the class capsules under the transfer learning framework. Experiments on SemEval datasets demonstrate the effectiveness of TransCap.
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis
Authors: Ziyao Lu, Jinsong Su, Yubin Ge, Linfeng Song, Le Sun, Jiebo Luo Jialong Tang
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: In aspect-level sentiment classification (ASC), it is prevalent to equip dominant neural models with attention mechanisms, for the sake of acquiring the importance of each context word on the given aspect. However, such a mechanism tends to excessively focus on a few frequent words with sentiment polarities, while ignoring infrequent ones. In this paper, we propose a progressive self-supervised attention learning approach for neural ASC models, which automatically mines useful attention supervision information from a training corpus to refine attention mechanisms. Specifically, we iteratively conduct sentiment predictions on all training instances. Particularly, at each iteration, the context word with the maximum attention weight is extracted as the one with active/misleading influence on the correct/incorrect prediction of every instance, and then the word itself is masked for subsequent iterations. Finally, we augment the conventional training objective with a regularization term, which enables ASC models to continue equally focusing on the extracted active context words while decreasing weights of those misleading ones. Experimental results on multiple datasets show that our proposed approach yields better attention mechanisms, leading to substantial improvements over the two state-of-the-art neural ASC models. Source code and trained models are available at r̆lhttps://github.com/DeepLearnXMU/PSSAttention.
Classification and Clustering of Arguments with Contextualized Word Embeddings
Authors: Benjamin Schiller, Tilman Beck, Johannes Daxenberger, Christian Stab, Iryna Gurevych Nils Reimers
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search. For the first time, we show how to leverage the power of contextualized word embeddings to classify and cluster topic-dependent arguments, achieving impressive results on both tasks and across multiple datasets. For argument classification, we improve the state-of-the-art for the UKP Sentential Argument Mining Corpus by 20.8 percentage points and for the IBM Debater - Evidence Sentences dataset by 7.4 percentage points. For the understudied task of argument clustering, we propose a pre-training step which improves by 7.8 percentage points over strong baselines on a novel dataset, and by 12.3 percentage points for the Argument Facet Similarity (AFS) Corpus.
Sentiment Tagging with Partial Labels using Modular Architectures
Authors: Dan Goldwasser Xiao Zhang
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Many NLP learning tasks can be decomposed into several distinct sub-tasks, each associated with a partial label. In this paper we focus on a popular class of learning problems, sequence prediction applied to several sentiment analysis tasks, and suggest a modular learning approach in which different sub-tasks are learned using separate functional modules, combined to perform the final task while sharing information. Our experiments show this approach helps constrain the learning process and can alleviate some of the supervision efforts.
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction
Authors: Tianrui Li, Bing Liu, Junbo Zhang Huaishao Luo
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity co-extraction. The former task is to extract aspects of a product or service from an opinion document, and the latter is to identify the polarity expressed in the document about these extracted aspects. Most existing algorithms address them as two separate tasks and solve them one by one, or only perform one task, which can be complicated for real applications. In this paper, we treat these two tasks as two sequence labeling problems and propose a novel Dual crOss-sharEd RNN framework (DOER) to generate all aspect term-polarity pairs of the input sentence simultaneously. Specifically, DOER involves a dual recurrent neural network to extract the respective representation of each task, and a cross-shared unit to consider the relationship between them. Experimental results demonstrate that the proposed framework outperforms state-of-the-art baselines on three benchmark datasets.
A Corpus for Modeling User and Language Effects in Argumentation on Online Debating
Authors: Claire Cardie Esin Durmus
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Existing argumentation datasets have succeeded in allowing researchers to develop computational methods for analyzing the content, structure and linguistic features of argumentative text. They have been much less successful in fostering studies of the effect of “user” traits --- characteristics and beliefs of the participants --- on the debate/argument outcome as this type of user information is generally not available. This paper presents a dataset of 78,376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles. We also complete an example study using the dataset to analyze the effect of selected user traits on the debate outcome in comparison to the linguistic features typically employed in studies of this kind.
Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese
Authors: Peifeng Li, Fang Kong, Qiaoming Zhu, Guodong Zhou Sheng Xu
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics. In this paper, we propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. In particular, besides encoding arguments (discourse units) using a gated convolutional network to obtain sentence-level representations, we train a simplified topic model to infer the latent topic-level representations. Moreover, we feed the two pairs of representations to two factored tensor networks, respectively, to capture both the sentence-level interactions and topic-level relevance using multi-slice tensors. Experimentation on CDTB, a Chinese discourse corpus, shows that our proposed model significantly outperforms several state-of-the-art baselines in both micro and macro F1-scores.
Learning from Omission
Authors: Noah Goodman Bill McDowell
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Pragmatic reasoning allows humans to go beyond the literal meaning when interpret- ing language in context. Previous work has shown that such reasoning can improve the performance of already-trained language understanding systems. Here, we explore whether pragmatic reasoning during training can improve the quality of learned meanings. Our experiments on reference game data show that end-to-end pragmatic training produces more accurate utterance interpretation models, especially when data is sparse and language is complex.
Multi-Task Learning for Coherence Modeling
Authors: Helen Yannakoudakis Youmna Farag
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We address the task of assessing discourse coherence, an aspect of text quality that is essential for many NLP tasks, such as summarization and language assessment. We propose a hierarchical neural network trained in a multi-task fashion that learns to predict a document-level coherence score (at the network`s top layers) along with word-level grammatical roles (at the bottom layers), taking advantage of inductive transfer between the two tasks. We assess the extent to which our framework generalizes to different domains and prediction tasks, and demonstrate its effectiveness not only on standard binary evaluation coherence tasks, but also on real-world tasks involving the prediction of varying degrees of coherence, achieving a new state of the art.
Data Programming for Learning Discourse Structure
Authors: Kate Thompson, Jean-Pierre Lorré, Nicholas Asher Sonia Badene
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: This paper investigates the advantages and limits of data programming for the task of learning discourse structure. The data programming paradigm implemented in the Snorkel framework allows a user to label training data using expert-composed heuristics, which are then transformed via the “generative step” into probability distributions of the class labels given the training candidates. These results are later generalized using a discriminative model. Snorkel`s attractive promise to create a large amount of annotated data from a smaller set of training data by unifying the output of a set of heuristics has yet to be used for computationally difficult tasks, such as that of discourse attachment, in which one must decide where a given discourse unit attaches to other units in a text in order to form a coherent discourse structure. Although approaching this problem using Snorkel requires significant modifications to the structure of the heuristics, we show that weak supervision methods can be more than competitive with classical supervised learning approaches to the attachment problem.
Evaluating Discourse in Structured Text Representations
Authors: Greg Durrett, Junyi Jessy Li, Katrin Erk Elisa Ferracane
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Discourse structure is integral to understanding a text and is helpful in many NLP tasks. Learning latent representations of discourse is an attractive alternative to acquiring expensive labeled discourse data. Liu and Lapata (2018) propose a structured attention mechanism for text classification that derives a tree over a text, akin to an RST discourse tree. We examine this model in detail, and evaluate on additional discourse-relevant tasks and datasets, in order to assess whether the structured attention improves performance on the end task and whether it captures a text`s discourse structure. We find the learned latent trees have little to no structure and instead focus on lexical cues; even after obtaining more structured trees with proposed model modifications, the trees are still far from capturing discourse structure when compared to discourse dependency trees from an existing discourse parser. Finally, ablation studies show the structured attention provides little benefit, sometimes even hurting performance.
Know What You Don`t Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories
Authors: David Schlangen Sina Zarrieß
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Zero-shot learning in Language & Vision is the task of correctly labelling (or naming) objects of novel categories. Another strand of work in L&V aims at pragmatically informative rather than “correct” object descriptions, e.g. in reference games. We combine these lines of research and model zero-shot reference games, where a speaker needs to successfully refer to a novel object in an image. Inspired by models of “rational speech acts”, we extend a neural generator to become a pragmatic speaker reasoning about uncertain object categories. As a result of this reasoning, the generator produces fewer nouns and names of distractor categories as compared to a literal speaker. We show that this conversational strategy for dealing with novel objects often improves communicative success, in terms of resolution accuracy of an automatic listener.
End-to-end Deep Reinforcement Learning Based Coreference Resolution
Authors: Xu Li, Dingcheng Li, Ping Li Hongliang Fei
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Recent neural network models have significantly advanced the task of coreference resolution. However, current neural coreference models are usually trained with heuristic loss functions that are computed over a sequence of local decisions. In this paper, we introduce an end-to-end reinforcement learning based coreference resolution model to directly optimize coreference evaluation metrics. Specifically, we modify the state-of-the-art higher-order mention ranking approach in Lee et al. (2018) to a reinforced policy gradient model by incorporating the reward associated with a sequence of coreference linking actions. Furthermore, we introduce maximum entropy regularization for adequate exploration to prevent the model from prematurely converging to a bad local optimum. Our proposed model achieves new state-of-the-art performance on the English OntoNotes v5.0 benchmark.
Implicit Discourse Relation Identification for Open-domain Dialogues
Authors: Kevin Bowden, Jiaqi Wu, Wen Cui, Marilyn Walker Mingyu Derek Ma
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of an open-domain dialogue system. Previous work primarily relies on a corpora of formal text which is inherently non-dialogic, i.e., news and journals. This data however is not suitable to handle the nuances of informal dialogue nor is it capable of navigating the plethora of valid topics present in open-domain dialogue. In this paper, we designed a novel discourse relation identification pipeline specifically tuned for open-domain dialogue systems. We firstly propose a method to automatically extract the implicit discourse relation argument pairs and labels from a dataset of dialogic turns, resulting in a novel corpus of discourse relation pairs; the first of its kind to attempt to identify the discourse relations connecting the dialogic turns in open-domain discourse. Moreover, we have taken the first steps to leverage the dialogue features unique to our task to further improve the identification of such relations by performing feature ablation and incorporating dialogue features to enhance the state-of-the-art model.
Coreference Resolution with Entity Equalization
Authors: Amir Globerson Ben Kantor
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: A key challenge in coreference resolution is to capture properties of entity clusters, and use those in the resolution process. Here we provide a simple and effective approach for achieving this, via an “Entity Equalization” mechanism. The Equalization approach represents each mention in a cluster via an approximation of the sum of all mentions in the cluster. We show how this can be done in a fully differentiable end-to-end manner, thus enabling high-order inferences in the resolution process. Our approach, which also employs BERT embeddings, results in new state-of-the-art results on the CoNLL-2012 coreference resolution task, improving average F1 by 3.6%.
A Cross-Domain Transferable Neural Coherence Model
Authors: Hamidreza Saghir, Jin Sung Kang, Teng Long, Avishek Joey Bose, Yanshuai Cao, Jackie Chi Kit Cheung Peng Xu
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain does not easily generalize to unseen categories of text. Previous work advocates for generative models for cross-domain generalization, because for discriminative models, the space of incoherent sentence orderings to discriminate against during training is prohibitively large. In this work, we propose a local discriminative neural model with a much smaller negative sampling space that can efficiently learn against incorrect orderings. The proposed coherence model is simple in structure, yet it significantly outperforms previous state-of-art methods on a standard benchmark dataset on the Wall Street Journal corpus, as well as in multiple new challenging settings of transfer to unseen categories of discourse on Wikipedia articles.
MOROCO: The Moldavian and Romanian Dialectal Corpus
Authors: Radu Tudor Ionescu Andrei Butnaru
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: In this work, we introduce the MOldavian and ROmanian Dialectal COrpus (MOROCO), which is freely available for download at r̆lhttps://github.com/butnaruandrei/MOROCO. The corpus contains 33564 samples of text (with over 10 million tokens) collected from the news domain. The samples belong to one of the following six topics: culture, finance, politics, science, sports and tech. The data set is divided into 21719 samples for training, 5921 samples for validation and another 5924 samples for testing. For each sample, we provide corresponding dialectal and category labels. This allows us to perform empirical studies on several classification tasks such as (i) binary discrimination of Moldavian versus Romanian text samples, (ii) intra-dialect multi-class categorization by topic and (iii) cross-dialect multi-class categorization by topic. We perform experiments using a shallow approach based on string kernels, as well as a novel deep approach based on character-level convolutional neural networks containing Squeeze-and-Excitation blocks. We also present and analyze the most discriminative features of our best performing model, before and after named entity removal.
Just “OneSeC” for Producing Multilingual Sense-Annotated Data
Authors: Tommaso Pasini, Roberto Navigli Bianca Scarlini
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: The well-known problem of knowledge acquisition is one of the biggest issues in Word Sense Disambiguation (WSD), where annotated data are still scarce in English and almost absent in other languages. In this paper we formulate the assumption of One Sense per Wikipedia Category and present OneSeC, a language-independent method for the automatic extraction of hundreds of thousands of sentences in which a target word is tagged with its meaning. Our automatically-generated data consistently lead a supervised WSD model to state-of-the-art performance when compared with other automatic and semi-automatic methods. Moreover, our approach outperforms its competitors on multilingual and domain-specific settings, where it beats the existing state of the art on all languages and most domains. All the training data are available for research purposes at r̆lhttp://trainomatic.org/onesec.
How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions
Authors: Robert Litschko, Sebastian Ruder, Ivan Vulić Goran Glavaš
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Cross-lingual word embeddings (CLEs) facilitate cross-lingual transfer of NLP models. Despite their ubiquitous downstream usage, increasingly popular projection-based CLE models are almost exclusively evaluated on bilingual lexicon induction (BLI). Even the BLI evaluations vary greatly, hindering our ability to correctly interpret performance and properties of different CLE models. In this work, we take the first step towards a comprehensive evaluation of CLE models: we thoroughly evaluate both supervised and unsupervised CLE models, for a large number of language pairs, on BLI and three downstream tasks, providing new insights concerning the ability of cutting-edge CLE models to support cross-lingual NLP. We empirically demonstrate that the performance of CLE models largely depends on the task at hand and that optimizing CLE models for BLI may hurt downstream performance. We indicate the most robust supervised and unsupervised CLE models and emphasize the need to reassess simple baselines, which still display competitive performance across the board. We hope our work catalyzes further research on CLE evaluation and model analysis.
SP-10K: A Large-scale Evaluation Set for Selectional Preference Acquisition
Authors: Hantian Ding, Yangqiu Song Hongming Zhang
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Selectional Preference (SP) is a commonly observed language phenomenon and proved to be useful in many natural language processing tasks. To provide a better evaluation method for SP models, we introduce SP-10K, a large-scale evaluation set that provides human ratings for the plausibility of 10,000 SP pairs over five SP relations, covering 2,500 most frequent verbs, nouns, and adjectives in American English. Three representative SP acquisition methods based on pseudo-disambiguation are evaluated with SP-10K. To demonstrate the importance of our dataset, we investigate the relationship between SP-10K and the commonsense knowledge in ConceptNet5 and show the potential of using SP to represent the commonsense knowledge. We also use the Winograd Schema Challenge to prove that the proposed new SP relations are essential for the hard pronoun coreference resolution problem.
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains
Authors: Anna Hätty, Marco Del Tredici, Sabine Schulte im Walde Dominik Schlechtweg
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We perform an interdisciplinary large-scale evaluation for detecting lexical semantic divergences in a diachronic and in a synchronic task: semantic sense changes across time, and semantic sense changes across domains. Our work addresses the superficialness and lack of comparison in assessing models of diachronic lexical change, by bringing together and extending benchmark models on a common state-of-the-art evaluation task. In addition, we demonstrate that the same evaluation task and modelling approaches can successfully be utilised for the synchronic detection of domain-specific sense divergences in the field of term extraction.