Title | Towards Emotion-Aware Agents For Negotiation Dialogues |
Publication Type | Conference Paper |
Year of Publication | 2021 |
Authors | Chawla, Kushal, Clever, Rene, Ramirez, Jaysa, Lucas, Gale, Gratch, Jonathan |
Conference Name | 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII) |
Date Published | sep |
Keywords | Adaptation models, affective computing, Computational modeling, Computer architecture, conversational agents, decision making, Deep Learning, emotion models, Human Behavior, human-agent interaction, Metrics, negotiation dialogues, outcome prediction, Predictive models, pubcrawl, Scalability |
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. |
DOI | 10.1109/ACII52823.2021.9597427 |
Citation Key | chawla_towards_2021 |