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.

本文提出了一种名为 SemSIn 的模型，通过模拟事件中心结构和事件关联结构之间的语义关系，更准确地识别事件之间的因果关系，并在三个广泛使用的数据集上取得了显着的性能提升。