Enhance Document-Level Event Argument Extraction Through Text Diffusion and Event Argument Constraints

Event Extraction (EE) is a critical task in space science, pivotal in supporting task analysis, knowledge discovery, and decision-making processes. The rapid development of China's manned space program has resulted in the emergence of a large volume of complex events and associated parameter information. While prompt tuning has demonstrated robust performance in sentence-level event extraction tasks within this domain, its effectiveness diminishes when applied to long-span event extraction across multiple documents. To address this challenge, we propose an enhanced prompt tuning method to improve the model's ability to fuse information and increase extraction accuracy. This approach is composed of two key components: 1) Text Diffusion Module: This module reconstructs noisy text based on encoded prompt statements, thereby augmenting the model's capacity for information integration and mitigating the issue of incomplete or noisy data in such environments. 2) Argument Constraint Module: Utilizing a Cross-Attention Transformer, this module integrates information from other event arguments, thereby enhancing extraction accuracy by addressing the problem of incomplete argument integration. Experimental results demonstrate the effectiveness of our proposed method, with improvements of 3.5% in the average F1 score for the Bart-base model and 3.0% for the Bart-large model across three distinct datasets. Additionally, ablation experiments confirm that these enhancements are both necessary and effective.