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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
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16 ACL 2017 Report Yuta Kikuchi, Sosuke Kobayashi 711 resource 99.38
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42 Transfer Learning - Machine Learnings Next Frontier Sebastian Ruder 978 tutorial 88.11
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53 Deep Learning from first principles in Python, R and Octave – Part 3 Tinniam V Ganesh 711 resource 83.82
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60 Rohan & Lenny #3: Recurrent Neural Networks & LSTMs Rohan Kapur 741 tutorial 82.56
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70 Discriminative Syntax-based Word Ordering for Text Generation Yue Zhang, Stephen Clark 999 paper 80.68
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92 A ‘Brief’ History of Neural Nets and Deep Learning, Part 4 Andrey Kurenkov 711 tutorial 76.88