PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues
Prajwal Vijay Kajare, Priyanshu Priya, Bikash Santra, Asif Ekbal

TL;DR
PRISMA is an interpretable, emotion-aware negotiation dialogue system that uses a novel reasoning mechanism and self-training to improve response quality in job interview and resource allocation scenarios.
Contribution
It introduces ENS-CoT for interpretability and curates two new datasets, enhancing emotionally intelligent negotiation responses with a self-training approach.
Findings
PRISMA improves interpretability of emotion-aware responses.
PRISMA enhances negotiation effectiveness in two domains.
Automatic and human evaluations confirm the system's improvements.
Abstract
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,…
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