PRISMA：基于偏好强化的自我训练方法用于可解释的具有情感智能的谈判对话
PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues
AI总结：
PRISMA通过引入情感感知谈判策略引导的推理机制，提升谈判对话的可解释性和情感响应能力，在招聘面试和资源分配领域实现更有效的谈判效果。
摘要：
情绪在塑造谈判结果中起着关键作用，影响信任、合作和长期关系。因此，开发能够识别并战略性回应情绪的谈判对话系统对于创建更有效的人本互动至关重要。除了生成情感适当响应外，可解释性——理解系统如何生成特定情绪感知响应——对于促进可靠性和建立关系至关重要。受这些因素驱动，本文介绍了PRISMA，一个旨在两个应用领域（招聘面试和资源分配）的可解释情感智能谈判对话系统。为了实现可解释性，我们提出了一个情感感知谈判策略引导的思考链（ENS-CoT）推理机制，该机制通过感知、理解和使用情绪来模拟人类谈判。利用ENS-CoT，我们创建了两个新数据集：JobNego（用于招聘面试谈判）和ResNego（用于资源分配谈判）。然后，我们利用这些数据集开发PRISMA，通过在自我训练中引入直接偏好优化（DPO）来指导代理生成更准确、可解释且情感适当的谈判响应。自动和人类评估表明，PRISMA显著提高了可解释性和生成适当情绪感知响应的能力，同时提高了整体谈判效果。
英文摘要：
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.
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。
