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        <title>Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection</title>
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    <name type="personal">
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            <namePart type="given">Yusuke</namePart>
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    <abstract>Due to the ability of encoding and mapping semantic information into a high-dimensional latent feature space, neural networks have been successfully used for detecting events to a certain extent. However, such a feature space can be easily contaminated by spurious features inherent in event detection. In this paper, we propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features. On the basis, we employ a recurrent network to eliminate the fakes. Detailed experiments on the ACE 2005 and TAC-KBP 2015 corpora show that our proposed method is highly effective and adaptable.</abstract>
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%0 Conference Proceedings
%T Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection
%A Hong, Yu
%A Zhou, Wenxuan
%A Zhang, Jingli
%A Zhou, Guodong
%A Zhu, Qiaoming
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F hong-etal-2018-self
%X Due to the ability of encoding and mapping semantic information into a high-dimensional latent feature space, neural networks have been successfully used for detecting events to a certain extent. However, such a feature space can be easily contaminated by spurious features inherent in event detection. In this paper, we propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features. On the basis, we employ a recurrent network to eliminate the fakes. Detailed experiments on the ACE 2005 and TAC-KBP 2015 corpora show that our proposed method is highly effective and adaptable.
%R 10.18653/v1/P18-1048
%U https://aclanthology.org/P18-1048
%U https://doi.org/10.18653/v1/P18-1048
%P 515-526

Markdown (Informal)

[Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection](https://aclanthology.org/P18-1048) (Hong et al., ACL 2018)

• Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection (Hong et al., ACL 2018)

ACL

• Yu Hong, Wenxuan Zhou, Jingli Zhang, Guodong Zhou, and Qiaoming Zhu. 2018. Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 515–526, Melbourne, Australia. Association for Computational Linguistics.