Towards Emotion-Aware Agents For Negotiation Dialogues

TL;DR

This paper investigates how emotion attributes from chat-based negotiations can improve the prediction of subjective goals like satisfaction and perception, aiding the development of emotion-aware negotiation agents.

Contribution

It analyzes the impact of emotion attributes on predicting negotiation outcomes, using deep learning and affective lexicons in a realistic camping scenario dataset.

Findings

Emotion attributes improve prediction accuracy of negotiation satisfaction.

Lexical and contextual emotions contribute significantly to outcome prediction.

Insights support designing adaptive, emotion-aware negotiation agents.

Abstract

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.