Prompt-Enhanced Prototype Framework for Few-shot Event Detection

Few-shot event detection (ED) aims at identifying and typing event mentions from text with limited annotations. Most existing methods for few-shot ED use event ontology and related knowledge to construct prototypes and fail to fully leverage the rich knowledge of pre-trained language models (PLMs) which could help improve the representation of prototypes. Motivated by this, we propose an prompt-enhanced prototype framework which combines prototype and prompt for few-shot ED. Considering the scarcity of labeled data, we also introduce contrastive learning to enrich prototypes. Specifically, we use heuristic rules to align FrameNet with annotated data to get corresponding prompts for each event and convert them into prompt prototype. We then leverage contrastive learning to aggregate event mentions into prototypes and maintain these prototypes for few-shot ED. Furthermore, We explore diverse prompt formats for representing prompt prototypes and introduce a more comprehensive lexical prompt which improves the performance of few-shot ED. We conduct extensive experiments on the MAVEN corpus to reveal the effectiveness of the proposed framework compared to state-of-the-art methods.