Revisiting Low-Resource Event Argument Extraction: Exploring Effective Use of LLMs for Data Augmentation

ACL ARR 2025 February Submission4854 Authors

Abstract: Event argument extraction (EAE) is a crucial task in information extraction. However, its performance heavily depends on expensive annotated data, making data scarcity a persistent challenge. Data augmentation serves as an effective approach to improving model performance in low-resource settings, yet research on applying LLMs for EAE augmentation remains preliminary. In this study, we pay attention to the boundary sensitivity of EAE and investigate four LLM-based augmentation strategies: argument replacement, adjunction rewriting, their combination, and annotation generation. Our experiments highlight the significance and effectiveness of enhancing argument diversity in low-resource EAE, with argument replacement demonstrating the best performance among all augmentation methods and surpassing the previous LLM-based approach. Additionally, we conduct a comprehensive evaluation from multiple perspectives, including task characteristics and data scale, providing valuable insights for the practical application of EAE in low-resource scenarios.

Paper Type: Long

Research Area: Information Extraction

Research Area Keywords: event extraction, few-shot extraction

Contribution Types: Approaches to low-resource settings

Languages Studied: English

Submission Number: 4854