Word-Label Alignment for Event Detection: A New Perspective via Optimal Transport

Abstract

Event Detection (ED) aims to identify mentions/triggers of real world events in text. In the literature, this task is modeled as a sequencelabeling or word-prediction problem. In this work, we present a novel formulation in which ED is modeled as a word-label alignment task. In particular, given the words in a sentence and possible event types, the objective is to infer an alignment matrix in which event trigger words are aligned with the most likely event types. Moreover, we show that this new perspective facilitates the incorporation of wordlabel alignment biases to improve alignment matrix for ED. Novel alignment biases and Optimal Transport are introduced to solve our alignment problem for ED. We conduct experiments on a benchmark dataset to demonstrate the effectiveness of the proposed model for ED.