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        <title>Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification</title>
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        <namePart type="given">Lianhui</namePart>
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        <namePart type="given">Zhisong</namePart>
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        <namePart type="given">Zhiting</namePart>
        <namePart type="family">Hu</namePart>
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    <name type="personal">
        <namePart type="given">Eric</namePart>
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            <namePart type="given">Min-Yen</namePart>
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    <abstract>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 framework 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.</abstract>
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%0 Conference Proceedings
%T Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
%A Qin, Lianhui
%A Zhang, Zhisong
%A Zhao, Hai
%A Hu, Zhiting
%A Xing, Eric
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F qin-etal-2017-adversarial
%X 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 framework 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.
%R 10.18653/v1/P17-1093
%U https://aclanthology.org/P17-1093
%U https://doi.org/10.18653/v1/P17-1093
%P 1006-1017

Markdown (Informal)

[Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification](https://aclanthology.org/P17-1093) (Qin et al., ACL 2017)

• Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification (Qin et al., ACL 2017)

ACL

• Lianhui Qin, Zhisong Zhang, Hai Zhao, Zhiting Hu, and Eric Xing. 2017. Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1006–1017, Vancouver, Canada. Association for Computational Linguistics.