Event Detection via Context Understanding Based on Multi-task Learning

Abstract

Event detection (ED) aims to identify events of interest described in the text. With the current explosive growth of text data on the internet, ED is increasingly practical and has gained many researchers’ attention. The existing works usually design ED as a token-level multi-class classification task. In this setting, given a sentence, ED models’ prediction for each token is relatively independent and thus cannot fully utilize sentence-level information and the association relations between multiple events in this sentence. To handle these situations, this paper proposes a multi-task learning based event detection model, which introduces an event type oriented text classification as an auxiliary task to improve the model’s understanding of sentence-level information. In addition, this model utilizes a Conditional Random Field (CRF) to explore the correlations between various event types and constrain the model’s output space. Experimental comparisons with state-of-the-art baselines on DuEE dataset demonstrate the model’s effectiveness.