@inproceedings{luo-etal-2025-rear,
    title = "{REAR}: Reinforced Reasoning Optimization for Event Argument Extraction with Relation-Aware Support",
    author = "Luo, Jianwen  and
      Hong, Yu  and
      Yang, Shuai  and
      Yao, Jianmin",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.421/",
    doi = "10.18653/v1/2025.findings-emnlp.421",
    pages = "7957--7972",
    ISBN = "979-8-89176-335-7",
    abstract = "Event argument extraction aims to identify event arguments and classify their roles within events, whereas relation extraction classifies semantic relationships between entities. Existing methods typically design task-specific models for EAE, which restricts the integration of relation-level semantics. Consequently, they overlook the complementary cues from RE that are beneficial for argument role disambiguation. To overcome this limitation, we propose REAR, a Relation-aware EAE Reinforced optimization framework. REAR first conducts joint supervised optimization on reasoning-enhanced data, which serves as a warm-up to strengthen the Large Language Model (LLM){'}s ability to perform EAE while incorporating auxiliary cues from RE. Subsequently, it applies reinforcement learning to explore diverse reasoning trajectories and derive near-optimal strategies for integrating relation-level signals into EAE. Experiments on the ACE-E, ACE-E$^+$ and ERE benchmarks demonstrate that REAR consistently surpasses previous decoder-only LLM methods, achieving F1-score gains of at least 0.9{\%}, 2.2{\%} and 1.6{\%}, respectively."
}