mtybilly/Instruct2DS huggingface dataset:
Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums
Authors: Zi Chai, Xinyu Xing, Xiaojun Wan, Bo Huang
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: 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
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: 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.
HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization
Authors: Xingxing Zhang, Furu Wei, Ming Zhou
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically using rule-based methods. Training the hierarchical encoder with these \textitinaccurate labels is challenging. Inspired by the recent work on pre-training transformer sentence encoders (Devlin et al., 2018), we propose Hibert (as shorthand for \textbfHIerachical \textbfBidirectional \textbfEncoder \textbfRepresentations from \textbfTransformers) for document encoding and a method to pre-train it using unlabeled data. We apply the pre-trained Hibert to our summarization model and it outperforms its randomly initialized counterpart by 1.25 ROUGE on the CNN/Dailymail dataset and by 2.0 ROUGE on a version of New York Times dataset. We also achieve the state-of-the-art performance on these two datasets.
Hierarchical Transformers for Multi-Document Summarization
Authors: Yang Liu, Mirella Lapata
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. We represent cross-document relationships via an attention mechanism which allows to share information as opposed to simply concatenating text spans and processing them as a flat sequence. Our model learns latent dependencies among textual units, but can also take advantage of explicit graph representations focusing on similarity or discourse relations. Empirical results on the WikiSum dataset demonstrate that the proposed architecture brings substantial improvements over several strong baselines.
Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization
Authors: Panagiotis Kouris, Georgios Alexandridis, Andreas Stafylopatis
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: 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.
Simple Unsupervised Summarization by Contextual Matching
Authors: Jiawei Zhou, Alexander Rush
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We propose an unsupervised method for sentence summarization using only language modeling. The approach employs two language models, one that is generic (i.e. pretrained), and the other that is specific to the target domain. We show that by using a product-of-experts criteria these are enough for maintaining continuous contextual matching while maintaining output fluency. Experiments on both abstractive and extractive sentence summarization data sets show promising results of our method without being exposed to any paired data.
Generating Summaries with Topic Templates and Structured Convolutional Decoders
Authors: Laura Perez-Beltrachini, Yang Liu, Mirella Lapata
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Existing neural generation approaches create multi-sentence text as a single sequence. In this paper we propose a structured convolutional decoder that is guided by the content structure of target summaries. We compare our model with existing sequential decoders on three data sets representing different domains. Automatic and human evaluation demonstrate that our summaries have better content coverage.
Morphological Irregularity Correlates with Frequency
Authors: Shijie Wu, Ryan Cotterell, Timothy O′Donnell
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We present a study of morphological irregularity. Following recent work, we define an information-theoretic measure of irregularity based on the predictability of forms in a language. Using a neural transduction model, we estimate this quantity for the forms in 28 languages. We first present several validatory and exploratory analyses of irregularity. We then show that our analyses provide evidence for a correlation between irregularity and frequency: higher frequency items are more likely to be irregular and irregular items are more likely be highly frequent. To our knowledge, this result is the first of its breadth and confirms longstanding proposals from the linguistics literature. The correlation is more robust when aggregated at the level of whole paradigms---providing support for models of linguistic structure in which inflected forms are unified by abstract underlying stems or lexemes.
Like a Baby: Visually Situated Neural Language Acquisition
Authors: Alexander Ororbia, Ankur Mali, Matthew Kelly, David Reitter
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We examine the benefits of visual context in training neural language models to perform next-word prediction. A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone with a 2% decrease in perplexity, even when no visual context is available at test. Fine-tuning the embeddings of a pre-trained state-of-the-art bidirectional language model (BERT) in the language modeling framework yields a 3.5% improvement. The advantage for training with visual context when testing without is robust across different languages (English, German and Spanish) and different models (GRU, LSTM, Delta-RNN, as well as those that use BERT embeddings). Thus, language models perform better when they learn like a baby, i.e, in a multi-modal environment. This finding is compatible with the theory of situated cognition: language is inseparable from its physical context.
Relating Simple Sentence Representations in Deep Neural Networks and the Brain
Authors: Sharmistha Jat, Hao Tang, Partha Talukdar, Tom Mitchell
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: What is the relationship between sentence representations learned by deep recurrent models against those encoded by the brain? Is there any correspondence between hidden layers of these recurrent models and brain regions when processing sentences? Can these deep models be used to synthesize brain data which can then be utilized in other extrinsic tasks? We investigate these questions using sentences with simple syntax and semantics (e.g., The bone was eaten by the dog.). We consider multiple neural network architectures, including recently proposed ELMo and BERT. We use magnetoencephalography (MEG) brain recording data collected from human subjects when they were reading these simple sentences. Overall, we find that BERT`s activations correlate the best with MEG brain data. We also find that the deep network representation can be used to generate brain data from new sentences to augment existing brain data. To the best of our knowledge, this is the first work showing that the MEG brain recording when reading a word in a sentence can be used to distinguish earlier words in the sentence. Our exploration is also the first to use deep neural network representations to generate synthetic brain data and to show that it helps in improving subsequent stimuli decoding task accuracy.
Modeling Affirmative and Negated Action Processing in the Brain with Lexical and Compositional Semantic Models
Authors: Vesna Djokic, Jean Maillard, Luana Bulat, Ekaterina Shutova
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: 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.
Word-order Biases in Deep-agent Emergent Communication
Authors: Rahma Chaabouni, Eugene Kharitonov, Alessandro Lazaric, Emmanuel Dupoux, Marco Baroni
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Sequence-processing neural networks led to remarkable progress on many NLP tasks. As a consequence, there has been increasing interest in understanding to what extent they process language as humans do. We aim here to uncover which biases such models display with respect to “natural” word-order constraints. We train models to communicate about paths in a simple gridworld, using miniature languages that reflect or violate various natural language trends, such as the tendency to avoid redundancy or to minimize long-distance dependencies. We study how the controlled characteristics of our miniature languages affect individual learning and their stability across multiple network generations. The results draw a mixed picture. On the one hand, neural networks show a strong tendency to avoid long-distance dependencies. On the other hand, there is no clear preference for the efficient, non-redundant encoding of information that is widely attested in natural language. We thus suggest inoculating a notion of “effort” into neural networks, as a possible way to make their linguistic behavior more human-like.
NNE: A Dataset for Nested Named Entity Recognition in English Newswire
Authors: Nicky Ringland, Xiang Dai, Ben Hachey, Sarvnaz Karimi, Cecile Paris, James R. Curran
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Named entity recognition (NER) is widely used in natural language processing applications and downstream tasks. However, most NER tools target flat annotation from popular datasets, eschewing the semantic information available in nested entity mentions. We describe NNE---a fine-grained, nested named entity dataset over the full Wall Street Journal portion of the Penn Treebank (PTB). Our annotation comprises 279,795 mentions of 114 entity types with up to 6 layers of nesting. We hope the public release of this large dataset for English newswire will encourage development of new techniques for nested NER.
Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks
Authors: Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Sequential labeling-based NER approaches restrict each word belonging to at most one entity mention, which will face a serious problem when recognizing nested entity mentions. In this paper, we propose to resolve this problem by modeling and leveraging the head-driven phrase structures of entity mentions, i.e., although a mention can nest other mentions, they will not share the same head word. Specifically, we propose Anchor-Region Networks (ARNs), a sequence-to-nuggets architecture for nested mention detection. ARNs first identify anchor words (i.e., possible head words) of all mentions, and then recognize the mention boundaries for each anchor word by exploiting regular phrase structures. Furthermore, we also design Bag Loss, an objective function which can train ARNs in an end-to-end manner without using any anchor word annotation. Experiments show that ARNs achieve the state-of-the-art performance on three standard nested entity mention detection benchmarks.
Improving Textual Network Embedding with Global Attention via Optimal Transport
Authors: Liqun Chen, Guoyin Wang, Chenyang Tao, Dinghan Shen, Pengyu Cheng, Xinyuan Zhang, Wenlin Wang, Yizhe Zhang, Lawrence Carin
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Constituting highly informative network embeddings is an essential tool for network analysis. It encodes network topology, along with other useful side information, into low dimensional node-based feature representations that can be exploited by statistical modeling. This work focuses on learning context-aware network embeddings augmented with text data. We reformulate the network embedding problem, and present two novel strategies to improve over traditional attention mechanisms: (i) a content-aware sparse attention module based on optimal transport; and (ii) a high-level attention parsing module. Our approach yields naturally sparse and self-normalized relational inference. It can capture long-term interactions between sequences, thus addressing the challenges faced by existing textual network embedding schemes. Extensive experiments are conducted to demonstrate our model can consistently outperform alternative state-of-the-art methods.
Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction
Authors: Yufang Hou, Charles Jochim, Martin Gleize, Francesca Bonin, Debasis Ganguly
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: While the fast-paced inception of novel tasks and new datasets helps foster active research in a community towards interesting directions, keeping track of the abundance of research activity in different areas on different datasets is likely to become increasingly difficult. The community could greatly benefit from an automatic system able to summarize scientific results, e.g., in the form of a leaderboard. In this paper we build two datasets and develop a framework (TDMS-IE) aimed at automatically extracting task, dataset, metric and score from NLP papers, towards the automatic construction of leaderboards. Experiments show that our model outperforms several baselines by a large margin. Our model is a first step towards automatic leaderboard construction, e.g., in the NLP domain.
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
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: 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
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: 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
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: 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 \textquoteleftIndications', \textquoteleftSymptoms', \textquoteleftFinding', \textquoteleftDisease', \textquoteleftDrug') 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.
Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems
Authors: Yanyan Zou, Wei Lu
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: An arithmetic word problem typically includes a textual description containing several constant quantities. The key to solving the problem is to reveal the underlying mathematical relations (such as addition and subtraction) among quantities, and then generate equations to find solutions. This work presents a novel approach, \textitQuantity Tagger, that automatically discovers such hidden relations by tagging each quantity with a \textitsign corresponding to one type of mathematical operation. For each quantity, we assume there exists a latent, variable-sized \textitquantity span surrounding the quantity token in the text, which conveys information useful for determining its sign. Empirical results show that our method achieves 5 and 8 points of accuracy gains on two datasets respectively, compared to prior approaches.
A Deep Reinforced Sequence-to-Set Model for Multi-Label Classification
Authors: Pengcheng Yang, Fuli Luo, Shuming Ma, Junyang Lin, Xu Sun
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: 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.
Joint Slot Filling and Intent Detection via Capsule Neural Networks
Authors: Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip Yu
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding. The existing works either treat slot filling and intent detection separately in a pipeline manner, or adopt joint models which sequentially label slots while summarizing the utterance-level intent without explicitly preserving the hierarchical relationship among words, slots, and intents. To exploit the semantic hierarchy for effective modeling, we propose a capsule-based neural network model which accomplishes slot filling and intent detection via a dynamic routing-by-agreement schema. A re-routing schema is proposed to further synergize the slot filling performance using the inferred intent representation. Experiments on two real-world datasets show the effectiveness of our model when compared with other alternative model architectures, as well as existing natural language understanding services.
Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision
Authors: Hongliang Dai, Yangqiu Song
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Lack of labeled training data is a major bottleneck for neural network based aspect and opinion term extraction on product reviews. To alleviate this problem, we first propose an algorithm to automatically mine extraction rules from existing training examples based on dependency parsing results. The mined rules are then applied to label a large amount of auxiliary data. Finally, we study training procedures to train a neural model which can learn from both the data automatically labeled by the rules and a small amount of data accurately annotated by human. Experimental results show that although the mined rules themselves do not perform well due to their limited flexibility, the combination of human annotated data and rule labeled auxiliary data can improve the neural model and allow it to achieve performance better than or comparable with the current state-of-the-art.
Cost-sensitive Regularization for Label Confusion-aware Event Detection
Authors: Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: In supervised event detection, most of the mislabeling occurs between a small number of confusing type pairs, including trigger-NIL pairs and sibling sub-types of the same coarse type. To address this label confusion problem, this paper proposes cost-sensitive regularization, which can force the training procedure to concentrate more on optimizing confusing type pairs. Specifically, we introduce a cost-weighted term into the training loss, which penalizes more on mislabeling between confusing label pairs. Furthermore, we also propose two estimators which can effectively measure such label confusion based on instance-level or population-level statistics. Experiments on TAC-KBP 2017 datasets demonstrate that the proposed method can significantly improve the performances of different models in both English and Chinese event detection.
Exploring Pre-trained Language Models for Event Extraction and Generation
Authors: Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, Dongsheng Li
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: 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%.
Improving Open Information Extraction via Iterative Rank-Aware Learning
Authors: Zhengbao Jiang, Pengcheng Yin, Graham Neubig
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Open information extraction (IE) is the task of extracting open-domain assertions from natural language sentences. A key step in open IE is confidence modeling, ranking the extractions based on their estimated quality to adjust precision and recall of extracted assertions. We found that the extraction likelihood, a confidence measure used by current supervised open IE systems, is not well calibrated when comparing the quality of assertions extracted from different sentences. We propose an additional binary classification loss to calibrate the likelihood to make it more globally comparable, and an iterative learning process, where extractions generated by the open IE model are incrementally included as training samples to help the model learn from trial and error. Experiments on OIE2016 demonstrate the effectiveness of our method. Code and data are available at r̆lhttps://github.com/jzbjyb/oie_rank.
Towards Improving Neural Named Entity Recognition with Gazetteers
Authors: Tianyu Liu, Jin-Ge Yao, Chin-Yew Lin
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Most of the recently proposed neural models for named entity recognition have been purely data-driven, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. This could increase the chance of overfitting since the models cannot access any supervision signal beyond the small amount of annotated data, limiting their power to generalize beyond the annotated entities. In this work, we show that properly utilizing external gazetteers could benefit segmental neural NER models. We add a simple module on the recently proposed hybrid semi-Markov CRF architecture and observe some promising results.
Span-Level Model for Relation Extraction
Authors: Kalpit Dixit, Yaser Al-Onaizan
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Relation Extraction is the task of identifying entity mention spans in raw text and then identifying relations between pairs of the entity mentions. Recent approaches for this span-level task have been token-level models which have inherent limitations. They cannot easily define and implement span-level features, cannot model overlapping entity mentions and have cascading errors due to the use of sequential decoding. To address these concerns, we present a model which directly models all possible spans and performs joint entity mention detection and relation extraction. We report a new state-of-the-art performance of 62.83 F1 (prev best was 60.49) on the ACE2005 dataset.
Enhancing Unsupervised Generative Dependency Parser with Contextual Information
Authors: Wenjuan Han, Yong Jiang, Kewei Tu
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Most of the unsupervised dependency parsers are based on probabilistic generative models that learn the joint distribution of the given sentence and its parse. Probabilistic generative models usually explicit decompose the desired dependency tree into factorized grammar rules, which lack the global features of the entire sentence. In this paper, we propose a novel probabilistic model called discriminative neural dependency model with valence (D-NDMV) that generates a sentence and its parse from a continuous latent representation, which encodes global contextual information of the generated sentence. We propose two approaches to model the latent representation: the first deterministically summarizes the representation from the sentence and the second probabilistically models the representation conditioned on the sentence. Our approach can be regarded as a new type of autoencoder model to unsupervised dependency parsing that combines the benefits of both generative and discriminative techniques. In particular, our approach breaks the context-free independence assumption in previous generative approaches and therefore becomes more expressive. Our extensive experimental results on seventeen datasets from various sources show that our approach achieves competitive accuracy compared with both generative and discriminative state-of-the-art unsupervised dependency parsers.
Neural Architectures for Nested NER through Linearization
Authors: Jana Straková, Milan Straka, Jan Hajic
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We propose two neural network architectures for nested named entity recognition (NER), a setting in which named entities may overlap and also be labeled with more than one label. We encode the nested labels using a linearized scheme. In our first proposed approach, the nested labels are modeled as multilabels corresponding to the Cartesian product of the nested labels in a standard LSTM-CRF architecture. In the second one, the nested NER is viewed as a sequence-to-sequence problem, in which the input sequence consists of the tokens and output sequence of the labels, using hard attention on the word whose label is being predicted. The proposed methods outperform the nested NER state of the art on four corpora: ACE-2004, ACE-2005, GENIA and Czech CNEC. We also enrich our architectures with the recently published contextual embeddings: ELMo, BERT and Flair, reaching further improvements for the four nested entity corpora. In addition, we report flat NER state-of-the-art results for CoNLL-2002 Dutch and Spanish and for CoNLL-2003 English.
Online Infix Probability Computation for Probabilistic Finite Automata
Authors: Marco Cognetta, Yo-Sub Han, Soon Chan Kwon
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Probabilistic finite automata (PFAs) are com- mon statistical language model in natural lan- guage and speech processing. A typical task for PFAs is to compute the probability of all strings that match a query pattern. An impor- tant special case of this problem is computing the probability of a string appearing as a pre- fix, suffix, or infix. These problems find use in many natural language processing tasks such word prediction and text error correction. Recently, we gave the first incremental algorithm to efficiently compute the infix probabilities of each prefix of a string (Cognetta et al., 2018). We develop an asymptotic improvement of that algorithm and solve the open problem of computing the infix probabilities of PFAs from streaming data, which is crucial when process- ing queries online and is the ultimate goal of the incremental approach.
How to Best Use Syntax in Semantic Role Labelling
Authors: Yufei Wang, Mark Johnson, Stephen Wan, Yifang Sun, Wei Wang
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: There are many different ways in which external information might be used in a NLP task. This paper investigates how external syntactic information can be used most effectively in the Semantic Role Labeling (SRL) task. We evaluate three different ways of encoding syntactic parses and three different ways of injecting them into a state-of-the-art neural ELMo-based SRL sequence labelling model. We show that using a constituency representation as input features improves performance the most, achieving a new state-of-the-art for non-ensemble SRL models on the in-domain CoNLL`05 and CoNLL`12 benchmarks.
PTB Graph Parsing with Tree Approximation
Authors: Yoshihide Kato, Shigeki Matsubara
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: The Penn Treebank (PTB) represents syntactic structures as graphs due to nonlocal dependencies. This paper proposes a method that approximates PTB graph-structured representations by trees. By our approximation method, we can reduce nonlocal dependency identification and constituency parsing into single tree-based parsing. An experimental result demonstrates that our approximation method with an off-the-shelf tree-based constituency parser significantly outperforms the previous methods in nonlocal dependency identification.
Sequence Labeling Parsing by Learning across Representations
Authors: Michalina Strzyz, David Vilares, Carlos Gómez-Rodríguez
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We use parsing as sequence labeling as a common framework to learn across constituency and dependency syntactic abstractions. To do so, we cast the problem as multitask learning (MTL). First, we show that adding a parsing paradigm as an auxiliary loss consistently improves the performance on the other paradigm. Secondly, we explore an MTL sequence labeling model that parses both representations, at almost no cost in terms of performance and speed. The results across the board show that on average MTL models with auxiliary losses for constituency parsing outperform single-task ones by 1.05 F1 points, and for dependency parsing by 0.62 UAS points.
A Prism Module for Semantic Disentanglement in Name Entity Recognition
Authors: Kun Liu, Shen Li, Daqi Zheng, Zhengdong Lu, Sheng Gao, Si Li
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Natural Language Processing has been perplexed for many years by the problem that multiple semantics are mixed inside a word, even with the help of context. To solve this problem, we propose a prism module to disentangle the semantic aspects of words and reduce noise at the input layer of a model. In the prism module, some words are selectively replaced with task-related semantic aspects, then these denoised word representations can be fed into downstream tasks to make them easier. Besides, we also introduce a structure to train this module jointly with the downstream model without additional data. This module can be easily integrated into the downstream model and significantly improve the performance of baselines on named entity recognition (NER) task. The ablation analysis demonstrates the rationality of the method. As a side effect, the proposed method also provides a way to visualize the contribution of each word.
Label-Agnostic Sequence Labeling by Copying Nearest Neighbors
Authors: Sam Wiseman, Karl Stratos
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Retrieve-and-edit based approaches to structured prediction, where structures associated with retrieved neighbors are edited to form new structures, have recently attracted increased interest. However, much recent work merely conditions on retrieved structures (e.g., in a sequence-to-sequence framework), rather than explicitly manipulating them. We show we can perform accurate sequence labeling by explicitly (and only) copying labels from retrieved neighbors. Moreover, because this copying is label-agnostic, we can achieve impressive performance in zero-shot sequence-labeling tasks. We additionally consider a dynamic programming approach to sequence labeling in the presence of retrieved neighbors, which allows for controlling the number of distinct (copied) segments used to form a prediction, and leads to both more interpretable and accurate predictions.
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset
Authors: Hannah Rashkin, Eric Michael Smith, Margaret Li, Y-Lan Boureau
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill. While it is straightforward for humans to recognize and acknowledge others' feelings in a conversation, this is a significant challenge for AI systems due to the paucity of suitable publicly-available datasets for training and evaluation. This work proposes a new benchmark for empathetic dialogue generation and EmpatheticDialogues, a novel dataset of 25k conversations grounded in emotional situations. Our experiments indicate that dialogue models that use our dataset are perceived to be more empathetic by human evaluators, compared to models merely trained on large-scale Internet conversation data. We also present empirical comparisons of dialogue model adaptations for empathetic responding, leveraging existing models or datasets without requiring lengthy re-training of the full model.
Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment
Authors: Siqi Bao, Huang He, Fan Wang, Rongzhong Lian, Hua Wu
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: In this paper, a novel Generation-Evaluation framework is developed for multi-turn conversations with the objective of letting both participants know more about each other. For the sake of rational knowledge utilization and coherent conversation flow, a dialogue strategy which controls knowledge selection is instantiated and continuously adapted via reinforcement learning. Under the deployed strategy, knowledge grounded conversations are conducted with two dialogue agents. The generated dialogues are comprehensively evaluated on aspects like informativeness and coherence, which are aligned with our objective and human instinct. These assessments are integrated as a compound reward to guide the evolution of dialogue strategy via policy gradient. Comprehensive experiments have been carried out on the publicly available dataset, demonstrating that the proposed method outperforms the other state-of-the-art approaches significantly.
Training Neural Response Selection for Task-Oriented Dialogue Systems
Authors: Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, ́ Nikola Mrkši\, Pei-Hao Su
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application being the low-data regime of most task-oriented dialogue tasks. Inspired by the recent success of pretraining in language modelling, we propose an effective method for deploying response selection in task-oriented dialogue. To train response selection models for task-oriented dialogue tasks, we propose a novel method which: 1) pretrains the response selection model on large general-domain conversational corpora; and then 2) fine-tunes the pretrained model for the target dialogue domain, relying only on the small in-domain dataset to capture the nuances of the given dialogue domain. Our evaluation on five diverse application domains, ranging from e-commerce to banking, demonstrates the effectiveness of the proposed training method.
Collaborative Dialogue in Minecraft
Authors: Anjali Narayan-Chen, Prashant Jayannavar, Julia Hockenmaier
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We wish to develop interactive agents that can communicate with humans to collaboratively solve tasks in grounded scenarios. Since computer games allow us to simulate such tasks without the need for physical robots, we define a Minecraft-based collaborative building task in which one player (A, the Architect) is shown a target structure and needs to instruct the other player (B, the Builder) to build this structure. Both players interact via a chat interface. A can observe B but cannot place blocks. We present the Minecraft Dialogue Corpus, a collection of 509 conversations and game logs. As a first step towards our goal of developing fully interactive agents for this task, we consider the subtask of Architect utterance generation, and show how challenging it is.
Neural Response Generation with Meta-words
Authors: Can Xu, Wei Wu, Chongyang Tao, Huang Hu, Matt Schuerman, Ying Wang
Published in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Main Conference Track), 2019

Abstract: We present open domain dialogue generation with meta-words. A meta-word is a structured record that describes attributes of a response, and thus allows us to explicitly model the one-to-many relationship within open domain dialogues and perform response generation in an explainable and controllable manner. To incorporate meta-words into generation, we propose a novel goal-tracking memory network that formalizes meta-word expression as a goal in response generation and manages the generation process to achieve the goal with a state memory panel and a state controller. Experimental results from both automatic evaluation and human judgment on two large-scale data sets indicate that our model can significantly outperform state-of-the-art generation models in terms of response relevance, response diversity, and accuracy of meta-word expression.