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Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification


Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation frame-work in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification. We develop an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator. Our method effectively transfers discriminability of connectives to the implicit features, and achieves state-of-the-art performance on the PDTB benchmark.


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# Title Author Topic Medium Score
1 Discourse Annotation: Discourse Connectives and Discourse Relations Aravind Joshi. Rashmi Prasad and Bonnie Webber 441 tutorial 195.13
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3 Multilingual Discourse Annotation Nianwen Xue 441 tutorial 177.96
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7 ICML 2018 Notes David Abel 999 survey 136.96
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10 Natural Language Processing for Intelligent Access to Scientific Information Francesco Ronzano, Horacio Saggion 232 tutorial 129.81
11 CS11-747 Neural Networks for NLP Adversarial Methods Zihang Dai & Qizhe Xie Graham Neubig 824 lecture 129.74
12 Predicate Argument Alignment using a Global Coherence Model Travis Wolfe, Mark Dredze, Benjamin Van Durme 999 paper 129.06
13 Modality and Negation in Natural Language Processing Roser Morante 112 tutorial 127.36
14 Opinion mining and sentiment analysis Bo Pang and Lillian Lee 381 survey 125.04
15 Sentiment Analysis and Subjectivity Bing Liu 381 survey 124.86
16 Word Sense Disambiguation: A Survey Roberto Navigli 391 survey 121.28
17 A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization William Yang Wang, Yashar Mehdad, Dragomir R. Radev, Amanda Stent 421 paper 120.89
18 Deep Reinforcement Learning: An Overview Yuxi Li 857 resource 120.77
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20 GANs for Simulation, Representation and Inference Ari Heljakka tutorial 119.37
21 Generative Adversarial Networks (GANs) Ian Goodfellow 341 tutorial 117.46
22 Generative Adversarial Networks (GANs) Ian Goodfellow 756 tutorial 117.46
23 Artificial Intelligence and Games Georgios N. Yannakakis and Julian Togelius 825 survey 117.09
24 Integrating Generative Lexicon and Lexical Semantic Resources James Pustejovsky, Elisabetta Jezek 353 survey 116.60
25 Learning Semantic Relations from Text Preslav Nakov, Diarmuid O' Seaghdha, Vivi Nastase, Stan Szpakowicz 353 tutorial 116.08
26 ACL 2017 Report Yuta Kikuchi, Sosuke Kobayashi 711 resource 115.19
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28 Toward Theoretical Understanding of Deep Learning Sanjeev Arora 711 tutorial 114.68
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32 The Logic of AMR Nathan Schneider, Jeff Flanigan, Tim O'Gorman tutorial 112.54
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34 Natural Language Processing Jacob Eisenstein 711 survey 112.22
35 Tutorial on Deep Generative Models Shakir Mohamed and Danilo Rezende 711 tutorial 111.83
36 Automatic Summarization Ani Nenkova and Kathleen McKeown 421 survey 111.82
37 Multilingual Relation Extraction using Compositional Universal Schema Patrick Verga, David Belanger, Emma Strubell, Benjamin Roth, Andrew... 999 paper 111.73
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41 Textual Entailment Ido Dagan, Dan Roth, Fabio Massimo Zanzotto 351 tutorial 110.62
42 Recent Developments of Content-Based Recommendation systems Giovanni Semeraro 999 tutorial 110.04
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44 Topics, Trends, and Resources in NLP Mohit Bansal 133 tutorial 109.16
45 Understanding Generative Adversarial Networks Balaji Lakshminarayanan 711 tutorial 108.80
46 Authorship Attribution Patrick Juola 965 survey 108.65
47 NLP’s generalization problem, and how researchers are tackling it Ana Marasovic 711 resource 107.96
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49 A Year In Computer Vision Benjamin F. Duffy, Daniel R. Flynn survey 107.76
50 An Introduction to Automatic Text Simplification Horacio Saggion 426 tutorial 107.38