EAGER：通过可验证奖励强化学习增强生成式事件抽取

Computer Science > Computation and Language arXiv:2609.29230 (cs) [Submitted on 24 Sep 2026] Title:EAGER: Enhancing Generative Event Extraction via Reinforcement Learning with Verifiable Rewards Authors:Omar Adjali, Siting Liang, Omair Shahzad Bhatti, Daniel Sonntag View a PDF of the paper titled EAGER: Enhancing Generative Event Extraction via Reinforcement Learning with Verifiable Rewards, by Omar Adjali and 3 other authors View PDF HTML (experimental) 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. Comments: Subjects: Computation and Language (cs.CL) Cite as: 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) Submission history From: Omar Adjali [view email] [v1] Thu, 24 Sep 2026 08:37:46 UTC (747 KB) Full-text links: Access Paper: View a PDF of the paper titled EAGER: Enhancing Generative Event Extraction via Reinforcement Learning with Verifiable Rewards, by Omar Adjali and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)