Event Causality Identification (ECI) aims to identify causal relations between events in unstructured texts. This is a very challenging task, because causal relations are usually expressed by implicit associations between events. Existing methods usually capture such associations by directly modeling the texts with pre-trained language models, which underestimate two kinds of semantic structures vital to the ECI task, namely, event-centric structure and event-associated structure. The former includes important semantic elements related to the events to describe them more precisely, while the latter contains semantic paths between two events to provide possible supports for ECI. In this paper, we study the implicit associations between events by modeling the above explicit semantic structures, and propose a Semantic Structure Integration model (SemSIn). It utilizes a GNN-based event aggregator to integrate the event-centric structure information, and employs an LSTM-based path aggregator to capture the event-associated structure information between two events. Experimental results on three widely used datasets show that SemSIn achieves significant improvements over baseline methods.

翻译：事件因果关系识别（Event Causality Identification，ECI）旨在识别非结构化文本中事件之间的因果关联。这是一项极具挑战性的任务，因为因果关系通常通过事件之间的隐式关联来表达。现有方法通常利用预训练语言模型直接对文本建模以捕获此类关联，但低估了对ECI任务至关重要的两类语义结构，即事件中心结构和事件关联结构。前者包含与事件相关的重要语义元素，以更精确地描述事件；后者包含两个事件之间的语义路径，为ECI提供可能的支持。本文通过建模上述显式语义结构来研究事件间的隐式关联，并提出一种语义结构集成模型（SemSIn）。该模型利用基于图神经网络（GNN）的事件聚合器整合事件中心结构信息，并采用基于长短期记忆网络（LSTM）的路径聚合器捕获两个事件之间的事件关联结构信息。在三个广泛使用的数据集上的实验结果表明，SemSIn在基线方法上取得了显著改进。