Unleashing Trigger-Free Event Detection: Revealing Event Correlations Via a Contrastive Derangement Framework

Abstract

Event detection (ED), detecting events with specified types observed in given texts, is critical to many downstream applications. Existing ED methods generally require high-quality triggers annotated by human experts, which is labor-intensive, especially for those nontrivial texts about breaking events. In this paper, we propose a novel trigger-free ED framework that detects multiple events from a given text without pre-defined triggers. Specifically, we first shed light on the event correlations with input texts using a joint embedding paradigm. Next, we devise derangement-based contrastive learning to model fine-grained correlations between multi-event instances. Since events in training benchmarks are usually imbalanced, we further design a simple yet effective event derangement module for balanced training. Experimental results on two benchmarks show that our trigger-free method is remarkably competitive to state-of-the-art trigger-based baselines.