自动化学报2018，Vol.44Issue(5)：811-818,8.DOI:10.16383/j.aas.2018.c170481
融合对抗学习的因果关系抽取
Causality Extraction With GAN
冯冲 1康丽琪 2石戈 1黄河燕2
作者信息
- 1. 北京理工大学计算机学院 北京100081
- 2. 北京理工大学自然语言处理实验室 北京100081
- 折叠
摘要
Abstract
Causality extraction is of important practical value in tasks such as event prediction, scenario generation, question answering, and textual implication; but most of the existing causality extraction methods require artificial definition of patterns and constraints and are heavily dependent on knowledge base. In this paper,the bidirectional gated recurrent units networks (BGRU) with attention mechanism are merged with confrontational learning by leveraging the confrontational learning characteristics of generative adversarial networks (GAN). Through redefining the generator and discriminator,the basic causality extraction network can construct a confrontation with the discriminator,and then obtain a high distinguishing feature from the causality interpretation information. Our experiments show that our approach leads to an improved performance over strong baselines.关键词
因果关系抽取/生成式对抗网络/注意力机制/对抗学习Key words
Causality extraction/generative adversarial network(GAN)/attention mechanism/adversarial learning引用本文复制引用
冯冲,康丽琪,石戈,黄河燕..融合对抗学习的因果关系抽取[J].自动化学报,2018,44(5):811-818,8.
