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        <title>Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification</title>
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        <namePart type="given">Hsin-Ping</namePart>
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    <abstract>Implicit discourse relations are not only more challenging to classify, but also to annotate, than their explicit counterparts. We tackle situations where training data for implicit relations are lacking, and exploit domain adaptation from explicit relations (Ji et al., 2015). We present an unsupervised adversarial domain adaptive network equipped with a reconstruction component. Our system outperforms prior works and other adversarial benchmarks for unsupervised domain adaptation. Additionally, we extend our system to take advantage of labeled data if some are available.</abstract>
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    <identifier type="doi">10.18653/v1/K19-1064</identifier>
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%0 Conference Proceedings
%T Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification
%A Huang, Hsin-Ping
%A Li, Junyi Jessy
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F huang-li-2019-unsupervised
%X Implicit discourse relations are not only more challenging to classify, but also to annotate, than their explicit counterparts. We tackle situations where training data for implicit relations are lacking, and exploit domain adaptation from explicit relations (Ji et al., 2015). We present an unsupervised adversarial domain adaptive network equipped with a reconstruction component. Our system outperforms prior works and other adversarial benchmarks for unsupervised domain adaptation. Additionally, we extend our system to take advantage of labeled data if some are available.
%R 10.18653/v1/K19-1064
%U https://aclanthology.org/K19-1064
%U https://doi.org/10.18653/v1/K19-1064
%P 686-695

Markdown (Informal)

[Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification](https://aclanthology.org/K19-1064) (Huang & Li, CoNLL 2019)

• Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification (Huang & Li, CoNLL 2019)

ACL

• Hsin-Ping Huang and Junyi Jessy Li. 2019. Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 686–695, Hong Kong, China. Association for Computational Linguistics.