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).

本文提出了CauSeRL方法，基于自我监督和对比转移策略，通过利用外部因果语句来识别事件因果关系，实现从环境上学习因果模式并提高模型的性能。实验结果表明，该方法在EventStoryLine 和Causal-TimeBank上比先前研究的方法分别提高2.0和3.4个F1值点。

通过对外部因果语句进行自监督表示学习来改进事件因果关系识别