Improving Event Causality Identification via Mutual Attention Based on Dependency Parsing

Yatian Shen, Songyang Wang, Eryan Zhang

Abstract: For event causality identification(ECI), many existing methods enhance the semantic information representation of inputs by using external knowledge or syntactic structures, while neglecting the mutual influence between event mentions. In this paper, we propose a new Mutual Attention based on Dependency Parsing(MADP) method, which combines the attention mechanism and the adjacency matrix obtained from the dependency parsing to enhance the connection between event mentions. Specifically, we design an attention mechanism, which includes trigger-aware attention and self-attention, to strengthen the connections between event mentions. The adjacency matrix helps our model eliminate the interference of redundant information on semantic information fusion. We evaluate our model on the dataset EventStoryLine(ESL) and achieve an F1 value of 65.1% , which outperforms the state-of-the-art model.

External IDs:dblp:conf/bdcloud/ShenWZ24