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

• DOI: 10.26599/bdma.2023.9020036
• Publication Date: 2024-04-22T17:46:40Z
• Authors: Junwen Duan; Xincheng Liao; Ying An; Jianxin Wang
• Keywords: multi-prompt learning (mpl); low-resource; Electronic computers. Computer science; 0202 electrical engineering, electronic engineering, information engineering; event extraction (ee); QA75.5-76.95; 02 engineering and technology; natural language processing

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 Keywords Extraction(EKE). We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language model. With the auxiliary sub-prompt, KeyEE learns event keywords knowledge implicitly, thereby reducing the dependence on annotated data. Furthermore, we investigate and analyze various EKE sub-prompt strategies to encourage further research in this area. Our experiments on benchmark datasets ACE2005 and ERE show that KeyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results.