Event Causality Identification (ECI) aims to detect whether there exists a causal relation between two events in a document. Existing studies adopt a kind of identifying after learning paradigm, where events' representations are first learned and then used for the identification. Furthermore, they mainly focus on the causality existence, but ignoring causal direction. In this paper, we take care of the causal direction and propose a new identifying while learning mode for the ECI task. We argue that a few causal relations can be easily identified with high confidence, and the directionality and structure of these identified causalities can be utilized to update events' representations for boosting next round of causality identification. To this end, this paper designs an *iterative learning and identifying framework*: In each iteration, we construct an event causality graph, on which events' causal structure representations are updated for boosting causal identification. Experiments on two public datasets show that our approach outperforms the state-of-the-art algorithms in both evaluations for causality existence identification and direction identification.

翻译：事件因果关系识别（ECI）旨在检测文档中两个事件之间是否存在因果关系。现有研究采用一种“先学习后识别”的范式，即先学习事件的表示，再将其用于识别。此外，这些研究主要关注因果关系的存在性，而忽略了因果方向。本文同时考虑因果方向，并为ECI任务提出一种新的“边学习边识别”模式。我们认为，少量因果关系可以较容易地以高置信度识别，这些已识别因果关系的方向性和结构可用于更新事件表示，从而提升下一轮因果识别的效果。为此，本文设计了一个*迭代学习与识别框架*：在每次迭代中，我们构建一个事件因果关系图，并基于该图更新事件的因果结构表示以增强因果识别。在两个公开数据集上的实验表明，我们的方法在因果关系存在性识别和方向识别的评估中均优于当前最先进的算法。