Collaborative Modeling for Document-level Event Argument Extraction

ICLR 2026 Conference Submission18250 Authors

Keywords: Event Extraction; Event Argument Extraction; Large Language Model

Abstract:

Document-level Event Argument Extraction (EAE) is hampered by two key chal-
lenges in long texts: ambiguity among co-occurring events and noise from irrel-
evant content. To address these issues, we propose CsEAE, a unified framework
that comprises two synergistic modules. The co-occurrence-aware module delin-
eates ambiguous event boundaries by modeling dependencies among co-occurring
events, while the structure-aware module filters noise by modeling trigger-centric
sentence relations. We further extend this framework to Large Language Models
(LLMs) with CsLLM, which distills these structural and co-occurrence cues into
tailored prompts. Trained on multiple datasets, CsLLM enhances the generaliza-
tion and performance of LLMs on the EAE task. On the RAMS, WikiEvents, and
MLEE benchmarks, CsEAE improves Arg-C F1 scores over the PAIE baseline by
2.1%, 2.3%, and 3.2%, respectively. Our LLM-based approach, CsLLM, achieves
even greater performance, demonstrating the effectiveness of our framework

Primary Area: generative models

Submission Number: 18250