Event causality identification is an important research task in natural language processing. Existing methods largely focus on identifying explicit causal relations, and give poor performance in implicit causalities, especially in the document level. In this paper, we formalize event causality identification as a graph-based edge prediction problem and propose a novel document-level context-based graph inference mechanism. Specifically, we use attention-based neural networks to automatically extract document-level contextual information, and a direction-sensitive graph inference mechanism to achieve information transfer and interaction among event causalities. Experimental results on the EventStoryLine v1.5 dataset show that our approach outperforms previous methods and baseline systems by a large margin in F1-score metrics (2.45% improvement on intra-sentence causalities and 3.08% improvement on cross-sentence causalities). Further analysis demonstrates that our model can effectively capture the document-level contextual information and latent causal information among events.

事件因果关系识别是自然语言处理中的重要研究任务。现有方法主要着眼于识别显式因果关系，并且在隐式因果关系（尤其是在文档级别）上表现不佳。在本文中，我们将事件因果关系识别形式化为基于图的边缘预测问题，并提出了一种新颖的基于文档级上下文的图推理机制。具体来说，我们使用基于注意力的神经网络自动提取文档级别的上下文信息，并使用方向敏感的图推理机制来实现事件因果关系之间的信息传递和交互。在EventStoryLine v1.5数据集上的实验结果表明，我们的方法在F1评分指标上大大优于以前的方法和基线系统（句子内因果关系改善2.45％，交叉句子因果关系改善3.08％）。