让模型先说理由再判断情感，提升准确率与可解释性
From Prediction to Justification: Aligning Sentiment Reasoning with Human Rationale via Reinforcement Learning
- 用强化学习让模型先生成情感理由再预测，模仿人类思考顺序
- 在四个数据集上，情感分类和三元组抽取均优于不带推理的基线
- 通过自我检测机制筛选难样本，提升模型在不确定情况下的可靠性
尽管基于方面的情感分析（ABSA）系统在识别情感极性方面已达到高准确率，但通常表现为“黑箱”，缺乏人类情感认知所具备的显式推理能力。人类不仅分类情感，还会构建判断背后的因果解释。为弥合这一差距，我们提出ABSA-R1，一种大型语言模型框架，旨在模拟“先推理后预测”的认知过程。通过强化学习（RL），ABSA-R1学会阐述其情感判断的依据，生成自然语言理由以支撑预测。我们引入一个与认知对齐的奖励模型，确保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，我们设计了一种性能驱动的拒绝采样策略，针对模型内部推理不确定或不一致的困难案例进行选择性优化。在四个基准上的实验表明，赋予模型这种显式推理能力不仅能增强可解释性，还能在情感分类和三元组提取任务上超越非推理基线，取得更优表现。
原文摘要 · Abstract (English)
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.
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