Towards Emotion-Aware Agents For Negotiation Dialogues
This work addresses the problem of enhancing virtual negotiation agents for pedagogy and conversational AI, though it is incremental as it builds on existing emotion analysis methods.
The paper tackled predicting outcome satisfaction and partner perception in negotiation dialogues by analyzing the contribution of emotion attributes beyond individual differences, using a dataset from a realistic camping scenario and achieving insights for designing adaptive agents.
Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the prediction of two important subjective goals in a negotiation - outcome satisfaction and partner perception. Specifically, we analyze the extent to which emotion attributes extracted from the negotiation help in the prediction, above and beyond the individual difference variables. We focus on a recent dataset in chat-based negotiations, grounded in a realistic camping scenario. We study three degrees of emotion dimensions - emoticons, lexical, and contextual by leveraging affective lexicons and a state-of-the-art deep learning architecture. Our insights will be helpful in designing adaptive negotiation agents that interact through realistic communication interfaces.