Utilizing Contextual Clues and Role Correlations for Enhancing Document-Level Event Argument Extraction

Wanlong Liu, Dingyi Zeng, Li Zhou, Wenyu Chen, Malu Zhang, Dan Liu, Xiaodong He, Haizhou Li

Abstract: Document-level event argument extraction is a crucial yet challenging task within the field of information extraction. Current mainstream approaches primarily focus on the information interaction between event triggers and their arguments, facing two limitations: insufficient context interaction and the ignorance of event correlations. Here, we introduce a novel framework named CARG (Contextual Aggregation of Clues and Role-based Latent Guidance), comprising two innovative components: the Contextual Clues Aggregation (CCA) and the Role-based Latent Information Guidance (RLIG) The CCA module leverages the attention weights derived from a pre-trained encoder to adaptively assimilate broader contextual information, while the RLIG module aims to capture the semantic correlations among event roles. We then instantiate the CARG framework into two variants based on two types of mainstream EAE approaches. Notably, our CARG framework introduces less than 1% new parameters yet significantly improves the performance. Comprehensive experiments across the RAMS, WikiEvents, MLEE and ACE 2005 datasets confirm the superiority of CARG, showing significant superiority in terms of both performance and inference speed compared to major benchmarks. Further analyses demonstrate the effectiveness of the proposed modules.

External IDs:doi:10.1109/taslpro.2025.3598644