REAR : Reinforced Reasoning Optimization for Event Argument Extraction with Relation-Aware Support (2025)

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.

## introduction event argument extraction (eae) is a crucial task in information extraction (ie), aimed at identifying and classifying arguments into predefined roles within event contexts @xcite . meanwhile, relation extraction (re) seeks to classify semantic relationships between pairs of entities @xcite . figure 1 provides an illustration of these two tasks. traditionally, eae and re have been addressed independently. existing approaches @xcite design separate models for eae without explicitly incorporating relation-level semantics, thereby overlooking the complementary cues that re can provide for argument role disambiguation. this isolation often leads to incomplete contextual understanding, especially in cases where entity interactions are crucial for accurate argument classification. indeed, re can provide valuable auxiliary information for eae. for instance, classifying the semantic relations between entities can offer cues for disambiguating their respective event roles. despite the auxiliary benefits of re, incorporating it to enhance eae via multi-task supervised fine-tuning (sft) remains challenging. sft suffers from the inability to fully exploit the semantic cues provided by re, as it relies on limited annotated reasoning paths @xcite . this limitation hinders the model's ability to capture the subtle interactions between event arguments and relational information, thereby restricting the performance gains for eae. in contrast, reinforcement learning (rl) offers a promising alternative. not only does rl enable dynamic exploration of diverse reasoning trajectories, but it also allows iterative trial-anderror to autonomously discover near-optimal solutions @xcite . through this process, rl can effectively integrate cross-task semantic reasoning to leverage re signals more comprehensively to strengthen eae. building on these observations, we propose rear, a two-stage relation-aware eae reinforced optimization framework designed to address the limitations of conventional sft. in the first stage, rear leverages reasoning-enhanced data to perform joint supervised optimization over eae and re, providing the large language model (llm) with a warm-up that facilitates reasoning. in the second stage, rl-based optimization is applied to transcend the constraints of static annotated reasoning paths. the llm dynamically explores diverse reasoning trajectories, engages in iterative trial-and-error to refine its decision-making process, and discovers near-optimal strategies for integrating relation-level semantics into eae. we experimentally validate rear on the ace-e, ace-e + and ere benchmark datasets @xcite . rear substantially outperforms previous decoder-only llm methods on these benchmarks. these results highlight the advantage of integrating relation-aware semantics with dynamic reasoning trajectories and underscore the effectiveness of rear in modeling cross-task interactions between eae and re tasks. ## approach in this section, we first formalize the eae and re tasks for reproducibility ( §2.1). subsequently, we detail our two-stage optimization strategy illustrated in figure 2 , joint supervised fine-tuning ( §2.2) and joint reinforced fine-tuning ( §2.3). ## conclusion in this paper, we propose a reinforced optimization framework designed to exploit semantic interactions between eae and re. our approach enhances semantic reasoning through initial joint supervised training with reasoning explanations, followed by joint reinforced optimization to dynamically explore richer reasoning trajectories. in the future, we plan to explore the effectiveness with alternative model architectures, such as encoderdecoder and encoder-only models. additionally, we aim to investigate rl methods that do not require large-scale reasoning annotated data, further enhancing the practical applicability of our rear.