Negotiation Dialogue System Using a Deep Learning-Based Parser

Kenjiro Morimoto, Katsuhide Fujita, Ken Watanabe

2025

Abstract

In recent years, there has been substantial research on negotiation dialogue agents. A notable study introduced a method that decoupled strategy from generation using dialogue acts that encapsulated the intent behind utterances. This approach has enhanced both the task success rate and the human-like quality of the generated responses. However, the rule-based implementation of the parser limits the types of sentences it can process for dialogue acts. Thus, this paper presents annotated training data based on the proposed dialogue acts and introduces a deep learning-based parser. The deep learning-based parser achieved a dialogue act classification accuracy of approximately 83% and effectively reduced the occurrence of unknown dialogue acts. Additionally, negotiation dialogue systems using deep learning-based parsers have demonstrated improved performance in terms of utility and fairness.

Paper Citation

in Harvard Style

Morimoto K., Fujita K. and Watanabe K. (2025). Negotiation Dialogue System Using a Deep Learning-Based Parser. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 135-143. DOI: 10.5220/0013245200003890

in Bibtex Style

@conference{icaart25,
author={Kenjiro Morimoto and Katsuhide Fujita and Ken Watanabe},
title={Negotiation Dialogue System Using a Deep Learning-Based Parser},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2025},
pages={135-143},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013245200003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Negotiation Dialogue System Using a Deep Learning-Based Parser
SN - 978-989-758-737-5
AU - Morimoto K.
AU - Fujita K.
AU - Watanabe K.
PY - 2025
SP - 135
EP - 143
DO - 10.5220/0013245200003890
PB - SciTePress