让对话系统懂情绪还说得清理由，提升谈判效果与可信度。
PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues
- 用模拟人类情绪管理的思维链机制增强可解释性。
- 在两个新数据集上显著提升情绪响应准确率与谈判成效。
- 适合需要透明决策的高信任场景，如求职面试与资源分配。
情绪对谈判结果有关键影响，塑造信任、合作与长期关系。开发能识别并策略性回应情绪的谈判对话系统，对构建以人为本的人机交互至关重要。除了生成恰当的情绪回应外，可解释性——即理解系统为何生成特定情绪响应——是建立可靠性与信任感的关键。为此，本文提出PRISMA，一个面向求职面试与资源分配两个应用场景的可解释情感智能谈判对话系统。为实现可解释性，我们设计了情绪感知的谈判策略思维链（ENS-CoT）机制，模拟人类在感知、理解、运用和管理情绪方面的过程。基于该机制，我们构建了两个新数据集：JobNego（求职谈判）和ResNego（资源分配谈判）。随后，通过引入直接偏好优化（DPO）增强自训练，使模型生成更准确、可解释且情绪恰当的回应。在两个数据集上的自动与人工评估表明，PRISMA显著提升了可解释性，生成了合适的情绪响应，并改善了整体谈判效果。
原文摘要 · Abstract (English)
Emotion plays a pivotal role in shaping negotiation outcomes, influencing trust, cooperation, and long-term relationships. Developing negotiation dialog systems that can recognize and respond strategically to emotions is, therefore, essential to create more effective human-centered interactions. Beyond generating emotionally appropriate responses, interpretability - understanding how a system generates a particular emotion-aware response, is critical for fostering reliability and building rapport. Driven by these aspects, in this work, we introduce PRISMA, an interpretable emotionally intelligent negotiation dialogue system targeting two application domains, viz. job interviews and resource allocation. To enable interpretability, we propose an Emotion-aware Negotiation Strategy-informed Chain-of-Thought (ENS-CoT) reasoning mechanism, which mimics human negotiation by perceiving, understanding, using, and managing emotions. Leveraging ENS-CoT, we curate two new datasets: JobNego (for job interview negotiation) and ResNego (for resource allocation negotiation). We then leverage these datasets to develop PRISMA by augmenting self-training with Direct Preference Optimization (DPO), guiding agents toward more accurate, interpretable, and emotionally appropriate negotiation responses. Automatic and human evaluation on JobNego and ResNego datasets demonstrate that PRISMA substantially enhances interpretability and generates appropriate emotion-aware responses, while improving overall negotiation effectiveness.
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