Semantic Structure Enhanced Event Causality Identification
Zhilei Hu Zixuan Li Xiaolong Jin Long Bai Saiping Guan Jiafeng Guo Xueqi Cheng
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.