Causality Extraction With GAN

Chong Feng*, Li Qi Kang, Ge Shi, He Yan Huang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)811-818
Number of pages8
JournalZidonghua Xuebao/Acta Automatica Sinica
Volume44
Issue number5
DOIs
Publication statusPublished - 1 May 2018

Keywords

  • Adversarial learning
  • Attention mechanism
  • Causality extraction
  • Generative adversarial network (GAN)

Fingerprint

Dive into the research topics of 'Causality Extraction With GAN'. Together they form a unique fingerprint.

Cite this

Feng, C., Kang, L. Q., Shi, G., & Huang, H. Y. (2018). Causality Extraction With GAN. Zidonghua Xuebao/Acta Automatica Sinica, 44(5), 811-818. https://doi.org/10.16383/j.aas.2018.c170481