@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T REAR: Reinforced Reasoning Optimization for Event Argument Extraction with Relation-Aware Support
%A Luo, Jianwen
%A Hong, Yu
%A Yang, Shuai
%A Yao, Jianmin
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F luo-etal-2025-rear
%X 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.
%U https://aclanthology.org/2025.findings-emnlp.421/
%P 7957-7972
Markdown (Informal)
[REAR: Reinforced Reasoning Optimization for Event Argument Extraction with Relation-Aware Support](https://aclanthology.org/2025.findings-emnlp.421/) (Luo et al., Findings 2025)
ACL