Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection
Yu Hong, Wenxuan Zhou, Jingli Zhang, Guodong Zhou, Qiaoming Zhu · 2018
Due to the ability of encoding and mapping semantic information into a highdimensional 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.