When designing rule-based dialogue systems, the need for the creation of an elaborate design by the designer is a challenge. One way to reduce the cost of creating content is to generate utterances from data collected in an objective and reproducible manner. This study focuses on rule-based dialogue systems using survey data and, more specifically, on opinion dialogue in which the system models the user. In the field of opinion dialogue, there has been little study on the topic of transition methods for modeling users while maintaining their motivation to engage in dialogue. To model them, we adopted information content. Our contribution includes the design of a rule-based dialogue system that does not require an elaborate design. We also reported an appropriate topic transition method based on information content. This is confirmed by the influence of the user’s personality characteristics. The content of the questions gives the user a sense of the system’s intention to understand them. We also reported the possibility that the system’s rational intention contributes to the user’s motivation to engage in dialogue with the system.
When designing rule-based dialogue systems, the need for elaborate design by the designer is a challenge for dialogue systems. One way to reduce the cost of creating content is to generate utterances from data collected in an objective and reproducible manner. This study focuses on rule-based dialogue systems using survey data, and more specifically on opinion dialogue where the system models the user. In opinion dialogue, there has not been much study of topic transition methods for modeling users while maintaining their motivation for dialogue. To model them, we adopted information content. Our contribution includes the design of a rule-based dialogue system that does not require elaborate design. We also reported an appropriate topic transition method based on information content. This is confirmed by the influence of the user's personality characteristics. The content of questions can give the user a sense of the system's intention to understand them. We also reported the possibility that the system's rational intention contributes to the user's motivation for dialogue with the system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.