KeyEE: Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt

Junwen Duan, Xincheng Liao, Ying An, Jianxin Wang

Abstract: Event Extraction (EE) is a key task in information extraction, which requires high-quality annotated data that are often costly to obtain. Traditional classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained annotations. While recent approaches have endeavored to address EE through a more data-efficient generative process, they often overlook event keywords, which are vital for EE. To tackle these challenges, we introduce KeyEE, a multi-prompt learning strategy that improves low-resource event extraction by Event