AHCI RESEARCH GROUP

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OUR RESEARCH

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2024

Zhang, Q.; Naradowsky, J.; Miyao, Y.

Self-Emotion Blended Dialogue Generation in Social Simulation Agents Proceedings Article

In: Kawahara, T.; Demberg, V.; Ultes, S.; Inoue, K.; Mehri, S.; Howcroft, D.; Komatani, K. (Ed.): pp. 228–247, Association for Computational Linguistics (ACL), 2024, ISBN: 9798891761612 (ISBN).

Abstract | Links | BibTeX | Tags: Agent behavior, Agents, Computational Linguistics, Decision making, Decisions makings, Dialogue generations, Dialogue strategy, Emotional state, Language Model, Model-driven, Natural language processing systems, Simulation framework, Social psychology, Social simulations, Speech processing, Virtual Reality, Virtual simulation environments

@inproceedings{zhang_self-emotion_2024,

title = {Self-Emotion Blended Dialogue Generation in Social Simulation Agents},

author = {Q. Zhang and J. Naradowsky and Y. Miyao},

editor = {T. Kawahara and V. Demberg and S. Ultes and K. Inoue and S. Mehri and D. Howcroft and K. Komatani},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105017744334&doi=10.18653%2Fv1%2F2024.sigdial-1.21&partnerID=40&md5=f185cfb5554eabfa85e6e956dfe6848e},

doi = {10.18653/v1/2024.sigdial-1.21},

isbn = {9798891761612 (ISBN)},

year  = {2024},

date = {2024-01-01},

pages = {228–247},

publisher = {Association for Computational Linguistics (ACL)},

abstract = {When engaging in conversations, dialogue agents in a virtual simulation environment may exhibit their own emotional states that are unrelated to the immediate conversational context, a phenomenon known as self-emotion. This study explores how such self-emotion affects the agents' behaviors in dialogue strategies and decision-making within a large language model (LLM)-driven simulation framework. In a dialogue strategy prediction experiment, we analyze the dialogue strategy choices employed by agents both with and without self-emotion, comparing them to those of humans. The results show that incorporating self-emotion helps agents exhibit more human-like dialogue strategies. In an independent experiment comparing the performance of models fine-tuned on GPT-4 generated dialogue datasets, we demonstrate that self-emotion can lead to better overall naturalness and humanness. Finally, in a virtual simulation environment where agents have discussions on multiple topics, we show that self-emotion of agents can significantly influence the decision-making process of the agents, leading to approximately a 50% change in decisions. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Agent behavior, Agents, Computational Linguistics, Decision making, Decisions makings, Dialogue generations, Dialogue strategy, Emotional state, Language Model, Model-driven, Natural language processing systems, Simulation framework, Social psychology, Social simulations, Speech processing, Virtual Reality, Virtual simulation environments},

pubstate = {published},

tppubtype = {inproceedings}

}