Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection

Yu Hong, Wenxuan Zhou, Jingli Zhang, Guodong Zhou, Qiaoming Zhu


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.
Anthology ID:
P18-1048
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Iryna Gurevych, Yusuke Miyao
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
515–526
Language:
URL:
https://aclanthology.org/P18-1048
DOI:
10.18653/v1/P18-1048
Bibkey:
Cite (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.
Cite (Informal):
Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection (Hong et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1048.pdf
Poster:
 P18-1048.Poster.pdf
Code
 JoeZhouWenxuan/Self-regulation-Employing-a-Generative-Adversarial-Network-to-Improve-Event-Detection