EAGER: Enhancing Generative Event Extraction via Reinforcement Learning with Verifiable Rewards

Abstract:End-to-end event extraction remains challenging for large language models as it requires simultaneous identification of event triggers, classification of event types, and extraction of schema-grounded argument spans. We present EAGER, a reinforcement learning framework for generative event extraction that combines fine-grained verifiable rewards with Schema-Contrastive Advantage Estimation to alleviate advantage collapse under sparse binary rewards. Our reward design explicitly targets structural validity, extraction accuracy, groundedness, coverage, over-generation, and span precision. Experiments across seven benchmark datasets show that EAGER consistently outperforms prompting, supervised fine-tuning, and prior reinforcement learning baselines, achieving a substantial improvement over the strongest prior method. Results demonstrate that task-aligned verifiable rewards and contrastive advantage estimation substantially improve structured extraction.

Computation and Language (cs.CL)

arXiv:2609.29230 [cs.CL]

(or arXiv:2609.29230v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.29230 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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