从预测到论证：通过强化学习对齐情感推理与人类推理
From Prediction to Justification: Aligning Sentiment Reasoning with Human Rationale via Reinforcement Learning
AI总结：
本文提出ABSA-R1框架，通过强化学习使模型具备生成自然语言论证的能力，提升情感分类和三元组提取性能。
摘要：
尽管基于方面的情感分析（ABSA）系统在识别情感极性方面准确率高，但往往作为"黑箱"运作，缺乏人类情感认知中的显式推理能力。人类不仅进行情感分类，还构建因果解释。为此，我们提出ABSA-R1，一个大型语言模型框架，旨在模仿"推理后再预测"的认知过程。通过强化学习（RL），ABSA-R1学习生成预测背后的理由，产生自然语言论证，以支撑其情感预测。我们引入了一个与认知对齐的奖励模型（原先是情感意识奖励模型），强制生成的推理路径与最终情感标签一致。此外，受元认知监控启发，我们实施了一种以性能驱动的拒绝采样策略，选择性地针对模型内部推理不确定或不一致的困难案例。在四个基准测试中的实验结果表明，给模型配备这种显式推理能力不仅增强了可解释性，还比非推理基线在情感分类和三元组提取任务中表现出更优的性能。
英文摘要：
While Aspect-based Sentiment Analysis (ABSA) systems have achieved high accuracy in identifying sentiment polarities, they often operate as "black boxes," lacking the explicit reasoning capabilities characteristic of human affective cognition. Humans do not merely categorize sentiment; they construct causal explanations for their judgments. To bridge this gap, we propose ABSA-R1, a large language model framework designed to mimic this ``reason-before-predict" cognitive process. By leveraging reinforcement learning (RL), ABSA-R1 learns to articulate the why behind the what, generating natural language justifications that ground its sentiment predictions. We introduce a Cognition-Aligned Reward Model (formerly sentiment-aware reward model) that enforces consistency between the generated reasoning path and the final emotional label. Furthermore, inspired by metacognitive monitoring, we implement a performance-driven rejection sampling strategy that selectively targets hard cases where the model's internal reasoning is uncertain or inconsistent. Experimental results on four benchmarks demonstrate that equipping models with this explicit reasoning capability not only enhances interpretability but also yields superior performance in sentiment classification and triplet extraction compared to non-reasoning baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
