@inproceedings{guo_roman2023,
title = {Personality-aware Natural Language Generation for Task-oriented Dialogue using Reinforcement Learning},
author = {Ao Guo and Atsumoto Ohashi and Yuya Chiba and Yuiko Tsunomori and Ryu Hirai and and Ryuichiro Higashinaka},
url = {https://ieeexplore.ieee.org/document/10309654},
year = {2023},
date = {2023-00-00},
urldate = {2023-00-00},
booktitle = {Proceedings of the 32st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) },
pages = {1823-1828},
address = {Busan, Korea},
abstract = {A task-oriented dialogue system capable of expressing personality can improve user engagement and satisfaction. To realize such a system, this paper presents a method of building a personality-aware natural language generation (NLG) module in task-oriented dialogue using reinforcement learning (RL). This method handles both the expression of personality and system intent. During the RL process, a positive reward is given when the generated utterance correctly expresses the assigned personality and conveys its system intent simultaneously. In our experiments on the MultiWOZ dataset, we fine-tuned a personality-aware NLG module for two personality traits (extraversion and neural perception sensitivity). We experimented with data from MultiWOZ and a user simulator to confirm its effectiveness in terms of the ability to express personality and task performance.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
