pwc-archive/papers-with-abstracts huggingface dataset:
Patterns of Argumentation Strategies across Topics
Authors: Khalid Al-Khatib, Henning Wachsmuth, Matthias Hagen, Benno Stein
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Argument Mining, BIG-bench Machine Learning

This paper presents an analysis of argumentation strategies in news editorials within and across topics. Given nearly 29,000 argumentative editorials from the New York Times, we develop two machine learning models, one for determining an editorial{'}s topic, and one for identifying evidence types in the editorial. Based on the distribution and structure of the identified types, we analyze the usage patterns of argumentation strategies among 12 different topics. We detect several common patterns that provide insights into the manifestation of argumentation strategies. Also, our experiments reveal clear correlations between the topics and the detected patterns.
Identifying attack and support argumentative relations using deep learning
Authors: Oana Cocarascu, Francesca Toni
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Argument Mining, Deep Learning, Word Embeddings

We propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to another, of the kind that naturally occur in a debate. The architecture uses two (unidirectional or bidirectional) Long Short-Term Memory networks and (trained or non-trained) word embeddings, and allows to considerably improve upon existing techniques that use syntactic features and supervised classifiers for the same form of (relation-based) argument mining.
Towards Compact and Fast Neural Machine Translation Using a Combined Method
Authors: Xiaowei Zhang, Wei Chen, Feng Wang, Shuang Xu, Bo Xu
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Language Modeling, Language Modelling, Machine Translation, NMT, Quantization, Translation

Neural Machine Translation (NMT) lays intensive burden on computation and memory cost. It is a challenge to deploy NMT models on the devices with limited computation and memory budgets. This paper presents a four stage pipeline to compress model and speed up the decoding for NMT. Our method first introduces a compact architecture based on convolutional encoder and weight shared embeddings. Then weight pruning is applied to obtain a sparse model. Next, we propose a fast sequence interpolation approach which enables the greedy decoding to achieve performance on par with the beam search. Hence, the time-consuming beam search can be replaced by simple greedy decoding. Finally, vocabulary selection is used to reduce the computation of softmax layer. Our final model achieves 10 times speedup, 17 times parameters reduction, less than 35MB storage size and comparable performance compared to the baseline model.
Instance Weighting for Neural Machine Translation Domain Adaptation
Authors: Rui Wang, Masao Utiyama, Lemao Liu, Kehai Chen, Eiichiro Sumita
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Domain Adaptation, Machine Translation, NMT, Sentence, Translation

Instance weighting has been widely applied to phrase-based machine translation domain adaptation. However, it is challenging to be applied to Neural Machine Translation (NMT) directly, because NMT is not a linear model. In this paper, two instance weighting technologies, i.e., sentence weighting and domain weighting with a dynamic weight learning strategy, are proposed for NMT domain adaptation. Empirical results on the IWSLT English-German/French tasks show that the proposed methods can substantially improve NMT performance by up to 2.7-6.7 BLEU points, outperforming the existing baselines by up to 1.6-3.6 BLEU points.
Source-Side Left-to-Right or Target-Side Left-to-Right? An Empirical Comparison of Two Phrase-Based Decoding Algorithms
Authors: Yin-Wen Chang, Michael Collins
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, Sentence, Translation

This paper describes an empirical study of the phrase-based decoding algorithm proposed by Chang and Collins (2017). The algorithm produces a translation by processing the source-language sentence in strictly left-to-right order, differing from commonly used approaches that build the target-language sentence in left-to-right order. Our results show that the new algorithm is competitive with Moses (Koehn et al., 2007) in terms of both speed and BLEU scores.
Using Target-side Monolingual Data for Neural Machine Translation through Multi-task Learning
Authors: Tobias Domhan, Felix Hieber
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Language Modeling, Language Modelling, Machine Translation, Multi-Task Learning, NMT, Translation

The performance of Neural Machine Translation (NMT) models relies heavily on the availability of sufficient amounts of parallel data, and an efficient and effective way of leveraging the vastly available amounts of monolingual data has yet to be found. We propose to modify the decoder in a neural sequence-to-sequence model to enable multi-task learning for two strongly related tasks: target-side language modeling and translation. The decoder predicts the next target word through two channels, a target-side language model on the lowest layer, and an attentional recurrent model which is conditioned on the source representation. This architecture allows joint training on both large amounts of monolingual and moderate amounts of bilingual data to improve NMT performance. Initial results in the news domain for three language pairs show moderate but consistent improvements over a baseline trained on bilingual data only.
Neural Semantic Parsing with Type Constraints for Semi-Structured Tables
Authors: Jayant Krishnamurthy, Pradeep Dasigi, Matt Gardner
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Entity Linking, Question Answering, Semantic Parsing, Vocal Bursts Type Prediction

We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key technical innovations: (1) a grammar for the decoder that only generates well-typed logical forms; and (2) an entity embedding and linking module that identifies entity mentions while generalizing across tables. We also introduce a novel method for training our neural model with question-answer supervision. On the WikiTableQuestions data set, our parser achieves a state-of-the-art accuracy of 43.3{\%} for a single model and 45.9{\%} for a 5-model ensemble, improving on the best prior score of 38.7{\%} set by a 15-model ensemble. These results suggest that type constraints and entity linking are valuable components to incorporate in neural semantic parsers.
Joint Concept Learning and Semantic Parsing from Natural Language Explanations
Authors: Shashank Srivastava, Igor Labutov, Tom Mitchell
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: General Classification, Semantic Parsing

Natural language constitutes a predominant medium for much of human learning and pedagogy. We consider the problem of concept learning from natural language explanations, and a small number of labeled examples of the concept. For example, in learning the concept of a phishing email, one might say {`}this is a phishing email because it asks for your bank account number{'}. Solving this problem involves both learning to interpret open ended natural language statements, and learning the concept itself. We present a joint model for (1) language interpretation (semantic parsing) and (2) concept learning (classification) that does not require labeling statements with logical forms. Instead, the model prefers discriminative interpretations of statements in context of observable features of the data as a weak signal for parsing. On a dataset of email-related concepts, our approach yields across-the-board improvements in classification performance, with a 30{\%} relative improvement in F1 score over competitive methods in the low data regime.
Detecting Perspectives in Political Debates
Authors: David Vilares, Yulan He
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Topic Models

We explore how to detect people{'}s perspectives that occupy a certain proposition. We propose a Bayesian modelling approach where topics (or propositions) and their associated perspectives (or viewpoints) are modeled as latent variables. Words associated with topics or perspectives follow different generative routes. Based on the extracted perspectives, we can extract the top associated sentences from text to generate a succinct summary which allows a quick glimpse of the main viewpoints in a document. The model is evaluated on debates from the House of Commons of the UK Parliament, revealing perspectives from the debates without the use of labelled data and obtaining better results than previous related solutions under a variety of evaluations.
``i have a feeling trump will win..................'': Forecasting Winners and Losers from User Predictions on Twitter
Authors: S Swamy, esh, Alan Ritter, Marie-Catherine de Marneffe
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Sentiment Analysis

Social media users often make explicit predictions about upcoming events. Such statements vary in the degree of certainty the author expresses toward the outcome: {``}Leonardo DiCaprio will win Best Actor{''} vs. {``}Leonardo DiCaprio may win{''} or {``}No way Leonardo wins!{''}. Can popular beliefs on social media predict who will win? To answer this question, we build a corpus of tweets annotated for veridicality on which we train a log-linear classifier that detects positive veridicality with high precision. We then forecast uncertain outcomes using the wisdom of crowds, by aggregating users{'} explicit predictions. Our method for forecasting winners is fully automated, relying only on a set of contenders as input. It requires no training data of past outcomes and outperforms sentiment and tweet volume baselines on a broad range of contest prediction tasks. We further demonstrate how our approach can be used to measure the reliability of individual accounts{'} predictions and retrospectively identify surprise outcomes.
A Question Answering Approach for Emotion Cause Extraction
Authors: Lin Gui, Jiannan Hu, Yulan He, Ruifeng Xu, Qin Lu, Jiachen Du
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Emotion Cause Extraction, Emotion Classification, Emotion Recognition, General Classification, Question Answering, Reading Comprehension, Word Embeddings

Emotion cause extraction aims to identify the reasons behind a certain emotion expressed in text. It is a much more difficult task compared to emotion classification. Inspired by recent advances in using deep memory networks for question answering (QA), we propose a new approach which considers emotion cause identification as a reading comprehension task in QA. Inspired by convolutional neural networks, we propose a new mechanism to store relevant context in different memory slots to model context information. Our proposed approach can extract both word level sequence features and lexical features. Performance evaluation shows that our method achieves the state-of-the-art performance on a recently released emotion cause dataset, outperforming a number of competitive baselines by at least 3.01{\%} in F-measure.
Story Comprehension for Predicting What Happens Next
Authors: Snigdha Chaturvedi, Haoruo Peng, Dan Roth
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Common Sense Reasoning, Natural Language Understanding, Question Answering, Reading Comprehension, Speaker Identification, Text Generation

Automatic story comprehension is a fundamental challenge in Natural Language Understanding, and can enable computers to learn about social norms, human behavior and commonsense. In this paper, we present a story comprehension model that explores three distinct semantic aspects: (i) the sequence of events described in the story, (ii) its emotional trajectory, and (iii) its plot consistency. We judge the model{'}s understanding of real-world stories by inquiring if, like humans, it can develop an expectation of what will happen next in a given story. Specifically, we use it to predict the correct ending of a given short story from possible alternatives. The model uses a hidden variable to weigh the semantic aspects in the context of the story. Our experiments demonstrate the potential of our approach to characterize these semantic aspects, and the strength of the hidden variable based approach. The model outperforms the state-of-the-art approaches and achieves best results on a publicly available dataset.
Opinion Recommendation Using A Neural Model
Authors: Zhongqing Wang, Yue Zhang
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: model, Sentiment Analysis

We present opinion recommendation, a novel task of jointly generating a review with a rating score that a certain user would give to a certain product which is unreviewed by the user, given existing reviews to the product by other users, and the reviews that the user has given to other products. A characteristic of opinion recommendation is the reliance of multiple data sources for multi-task joint learning. We use a single neural network to model users and products, generating customised product representations using a deep memory network, from which customised ratings and reviews are constructed jointly. Results show that our opinion recommendation system gives ratings that are closer to real user ratings on Yelp.com data compared with Yelp{'}s own ratings. our methods give better results compared to several pipelines baselines.
Efficient Discontinuous Phrase-Structure Parsing via the Generalized Maximum Spanning Arborescence
Authors: Caio Corro, Joseph Le Roux, Mathieu Lacroix
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Dependency Parsing

We present a new method for the joint task of tagging and non-projective dependency parsing. We demonstrate its usefulness with an application to discontinuous phrase-structure parsing where decoding lexicalized spines and syntactic derivations is performed jointly. The main contributions of this paper are (1) a reduction from joint tagging and non-projective dependency parsing to the Generalized Maximum Spanning Arborescence problem, and (2) a novel decoding algorithm for this problem through Lagrangian relaxation. We evaluate this model and obtain state-of-the-art results despite strong independence assumptions.
Incremental Graph-based Neural Dependency Parsing
Authors: Xiaoqing Zheng
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Dependency Parsing, Transition-Based Dependency Parsing

Very recently, some studies on neural dependency parsers have shown advantage over the traditional ones on a wide variety of languages. However, for graph-based neural dependency parsing systems, they either count on the long-term memory and attention mechanism to implicitly capture the high-order features or give up the global exhaustive inference algorithms in order to harness the features over a rich history of parsing decisions. The former might miss out the important features for specific headword predictions without the help of the explicit structural information, and the latter may suffer from the error propagation as false early structural constraints are used to create features when making future predictions. We explore the feasibility of explicitly taking high-order features into account while remaining the main advantage of global inference and learning for graph-based parsing. The proposed parser first forms an initial parse tree by head-modifier predictions based on the first-order factorization. High-order features (such as grandparent, sibling, and uncle) then can be defined over the initial tree, and used to refine the parse tree in an iterative fashion. Experimental results showed that our model (called INDP) archived competitive performance to existing benchmark parsers on both English and Chinese datasets.
Neural Discontinuous Constituency Parsing
Authors: Milo{\v{s}} Stanojevi{\'c}, Raquel G. Alhama
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Constituency Parsing

One of the most pressing issues in discontinuous constituency transition-based parsing is that the relevant information for parsing decisions could be located in any part of the stack or the buffer. In this paper, we propose a solution to this problem by replacing the structured perceptron model with a recursive neural model that computes a global representation of the configuration, therefore allowing even the most remote parts of the configuration to influence the parsing decisions. We also provide a detailed analysis of how this representation should be built out of sub-representations of its core elements (words, trees and stack). Additionally, we investigate how different types of swap oracles influence the results. Our model is the first neural discontinuous constituency parser, and it outperforms all the previously published models on three out of four datasets while on the fourth it obtains second place by a tiny difference.
Stack-based Multi-layer Attention for Transition-based Dependency Parsing
Authors: Zhirui Zhang, Shujie Liu, Mu Li, Ming Zhou, Enhong Chen
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Dependency Parsing, Language Modeling, Language Modelling, Machine Translation, Text Summarization, Transition-Based Dependency Parsing, Translation

Although sequence-to-sequence (seq2seq) network has achieved significant success in many NLP tasks such as machine translation and text summarization, simply applying this approach to transition-based dependency parsing cannot yield a comparable performance gain as in other state-of-the-art methods, such as stack-LSTM and head selection. In this paper, we propose a stack-based multi-layer attention model for seq2seq learning to better leverage structural linguistics information. In our method, two binary vectors are used to track the decoding stack in transition-based parsing, and multi-layer attention is introduced to capture multiple word dependencies in partial trees. We conduct experiments on PTB and CTB datasets, and the results show that our proposed model achieves state-of-the-art accuracy and significant improvement in labeled precision with respect to the baseline seq2seq model.
Semi-supervised Structured Prediction with Neural CRF Autoencoder
Authors: Xiao Zhang, Yong Jiang, Hao Peng, Kewei Tu, Dan Goldwasser
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Part-Of-Speech Tagging, POS, POS Tagging, Prediction, Structured Prediction

In this paper we propose an end-to-end neural CRF autoencoder (NCRF-AE) model for semi-supervised learning of sequential structured prediction problems. Our NCRF-AE consists of two parts: an encoder which is a CRF model enhanced by deep neural networks, and a decoder which is a generative model trying to reconstruct the input. Our model has a unified structure with different loss functions for labeled and unlabeled data with shared parameters. We developed a variation of the EM algorithm for optimizing both the encoder and the decoder simultaneously by decoupling their parameters. Our Experimental results over the Part-of-Speech (POS) tagging task on eight different languages, show that our model can outperform competitive systems in both supervised and semi-supervised scenarios.
TAG Parsing with Neural Networks and Vector Representations of Supertags
Authors: Jungo Kasai, Bob Frank, Tom McCoy, Owen Rambow, Alexis Nasr
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Sentence, TAG

We present supertagging-based models for Tree Adjoining Grammar parsing that use neural network architectures and dense vector representation of supertags (elementary trees) to achieve state-of-the-art performance in unlabeled and labeled attachment scores. The shift-reduce parsing model eschews lexical information entirely, and uses only the 1-best supertags to parse a sentence, providing further support for the claim that supertagging is {``}almost parsing.{''} We demonstrate that the embedding vector representations the parser induces for supertags possess linguistically interpretable structure, supporting analogies between grammatical structures like those familiar from recent work in distributional semantics. This dense representation of supertags overcomes the drawbacks for statistical models of TAG as compared to CCG parsing, raising the possibility that TAG is a viable alternative for NLP tasks that require the assignment of richer structural descriptions to sentences.
End-to-End Neural Relation Extraction with Global Optimization
Authors: Meishan Zhang, Yue Zhang, Guohong Fu
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: General Classification, global-optimization, Relation, Relation Extraction, Representation Learning, Structured Prediction

Neural networks have shown promising results for relation extraction. State-of-the-art models cast the task as an end-to-end problem, solved incrementally using a local classifier. Yet previous work using statistical models have demonstrated that global optimization can achieve better performances compared to local classification. We build a globally optimized neural model for end-to-end relation extraction, proposing novel LSTM features in order to better learn context representations. In addition, we present a novel method to integrate syntactic information to facilitate global learning, yet requiring little background on syntactic grammars thus being easy to extend. Experimental results show that our proposed model is highly effective, achieving the best performances on two standard benchmarks.
KGEval: Accuracy Estimation of Automatically Constructed Knowledge Graphs
Authors: Prakhar Ojha, Partha Talukdar
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Diversity, Knowledge Graphs

Automatic construction of large knowledge graphs (KG) by mining web-scale text datasets has received considerable attention recently. Estimating accuracy of such automatically constructed KGs is a challenging problem due to their size and diversity. This important problem has largely been ignored in prior research {--} we fill this gap and propose KGEval. KGEval uses coupling constraints to bind facts and crowdsources those few that can infer large parts of the graph. We demonstrate that the objective optimized by KGEval is submodular and NP-hard, allowing guarantees for our approximation algorithm. Through experiments on real-world datasets, we demonstrate that KGEval best estimates KG accuracy compared to other baselines, while requiring significantly lesser number of human evaluations.
Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short
Authors: Jay Pujara, Eriq Augustine, Lise Getoor
Date: 2017-09-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Knowledge Graph Embeddings, Knowledge Graphs, Question Answering

Knowledge graph (KG) embedding techniques use structured relationships between entities to learn low-dimensional representations of entities and relations. One prominent goal of these approaches is to improve the quality of knowledge graphs by removing errors and adding missing facts. Surprisingly, most embedding techniques have been evaluated on benchmark datasets consisting of dense and reliable subsets of human-curated KGs, which tend to be fairly complete and have few errors. In this paper, we consider the problem of applying embedding techniques to KGs extracted from text, which are often incomplete and contain errors. We compare the sparsity and unreliability of different KGs and perform empirical experiments demonstrating how embedding approaches degrade as sparsity and unreliability increase.