Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement

• URL: http://arxiv.org/abs/2106.01654v1
• Date: Thu, 3 Jun 2021 07:50:50 GMT
• Title: Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement
• Authors: Xinyu Zuo, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Weihua Peng and Yuguang Chen
• Abstract summary: We propose CauSeRL, which leverages external causal statements for event causality identification. First of all, we design a self-supervised framework to learn context-specific causal patterns from external causal statements. We adopt a contrastive transfer strategy to incorporate the learned context-specific causal patterns into the target ECI model.
• Score: 17.77752074834281
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Current models for event causality identification (ECI) mainly adopt a supervised framework, which heavily rely on labeled data for training. Unfortunately, the scale of current annotated datasets is relatively limited, which cannot provide sufficient support for models to capture useful indicators from causal statements, especially for handing those new, unseen cases. To alleviate this problem, we propose a novel approach, shortly named CauSeRL, which leverages external causal statements for event causality identification. First of all, we design a self-supervised framework to learn context-specific causal patterns from external causal statements. Then, we adopt a contrastive transfer strategy to incorporate the learned context-specific causal patterns into the target ECI model. Experimental results show that our method significantly outperforms previous methods on EventStoryLine and Causal-TimeBank (+2.0 and +3.4 points on F1 value respectively).

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