Technology Category
Knowledge Representation and Reasoning: ArgumentationNatural Language Processing: Safety and RobustnessReasoning under Uncertainty: Relational Probabilistic Models
R1-RE: Cross-Domain Relationship Extraction with RLVR
🤖 AI Summary
Relation extraction (RE) exhibits poor generalization in out-of-domain (OOD) settings, as conventional supervised methods suffer from domain bias. To address this, we propose R1-RE—a novel framework that reformulates RE as a multi-step reasoning task guided by human-written annotation guidelines. R1-RE introduces verifiable-reward reinforcement learning (RLVR), the first RL paradigm explicitly designed to emulate human annotation logic by dynamically validating and optimizing intermediate reasoning steps. Crucially, it leverages compact language models (e.g., 7B-parameter variants) augmented with external annotation guidelines as structured prior knowledge, thereby enhancing cross-domain robustness without scaling model size. Evaluated on SemEval-2010 and MDKG, R1-RE-7B achieves an average cross-domain accuracy of 70%, matching the performance of large proprietary models such as GPT-4o. This demonstrates that controllable, guideline-driven reasoning enables lightweight models to attain strong OOD generalization—establishing a new paradigm for efficient and interpretable RE.
Technology Category
Application Category
📝 Abstract
Relationship extraction (RE) is a core task in natural language processing. Traditional approaches typically frame RE as a supervised learning problem, directly mapping context to labels-an approach that often suffers from poor out-of-domain (OOD) generalization. Inspired by the workflow of human annotators, we reframe RE as a reasoning task guided by annotation guidelines and introduce R1-RE, the first reinforcement learning with verifiable reward (RLVR) framework for RE tasks. Our method elicits the reasoning abilities of small language models for annotation tasks, resulting in significantly improved OOD robustness. We evaluate our approach on the public Sem-2010 dataset and a private MDKG dataset. The R1-RE-7B model attains an average OOD accuracy of approximately 70%, on par with leading proprietary models such as GPT-4o. Additionally, our comprehensive analysis provides novel insights into the training dynamics and emergent reasoning behaviors of the RLVR paradigm for RE.
Problem
Research questions and friction points this paper is trying to address.
Improves out-of-domain generalization in relationship extraction
Reframes RE as reasoning task with verifiable reinforcement learning
Enhances small models' reasoning for annotation tasks
Innovation
Methods, ideas, or system contributions that make the work stand out.
Reinforcement learning with verifiable reward framework
Reframing RE as reasoning task with guidelines
Improved OOD robustness via small language models
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