@InProceedings{hong-EtAl:2018:Long,
  author    = {Hong, Yu  and  Zhou, Wenxuan  and  zhang, jingli  and  Zhou, Guodong  and  Zhu, Qiaoming},
  title     = {Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {515--526},
  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.},
  url       = {http://www.aclweb.org/anthology/P18-1048}
}