Fusion meets Function: The Adaptive Selection-Generation Approach in Event Argument Extraction

Guoxuan Ding, Xiaobo Guo, Xin Wang, Lei Wang, Tianshu Fu, Nan Mu, Daren Zha

Abstract: Event Argument Extraction is a critical task of Event Extraction, focused on identifying event arguments within text. This paper presents a novel Fusion Selection-Generation-Based Approach, by combining the precision of selective methods with the semantic generation capability of generative methods to enhance argument extraction accuracy. This synergistic integration, achieved through fusion prompt, element-based extraction, and fusion learning, addresses the challenges of input, process, and output fusion, effectively blending the unique characteristics of both methods into a cohesive model. Comprehensive evaluations on the RAMS and WikiEvents demonstrate the model’s state-of-the-art performance and efficiency.