Employing Multiple Decomposable Attention Networks to Resolve Event Coreference

Abstract

Event coreference resolution is a challenging NLP task due to this task needs to understand the semantics of events. Different with most previous studies used probability-based or graph-based models, this paper introduces a novel neural network, MDAN (Multiple Decomposable Attention Networks), to resolve document-level event coreference from different views, i.e., event mention, event arguments and trigger context. Moreover, it applies a document-level global inference mechanism to further resolve the coreference chains. The experimental results on two popular datasets ACE and TAC-KBP illustrate that our model outperforms the two state-of-the-art baselines.