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    <abstract>In our research, we aim to achieve SDS capable of generating responses considering user preferences. While users have individual topic preferences, existing SDSs do not adequately consider such information. With the development of LLMs, SDSs are expected to be implemented in various tasks, including coexisting with humans in robotic applications. To become better partners with humans, systems are anticipated to memorize user preferences and utilize them in their response generation. Our future reserarch aim to realize SDSs that can remember and complement user information through dialogue, enabling personalized interactions. In YRRSDS, The author would like to propose the following topics for discussion. 1. What is the necessity of SDSs aimed specifically at dialogue rather than being just user interfaces? What do general users need from SDSs through conversation? 2. The relationship between SDSs and users: Should SDSs act just as agents, or should they aim to become like friends or family? 3. Privacy in conversational content. Nowadays, many SDS applications operate online via APIs, but is this preferable from a privacy perspective? If it is not preferable, how can this issue be resolved?</abstract>
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%0 Conference Proceedings
%T Personalized Topic Transition for Dialogue System
%A Yoshida, Kai
%Y Inoue, Koji
%Y Fu, Yahui
%Y Axelsson, Agnes
%Y Ohashi, Atsumoto
%Y Madureira, Brielen
%Y Zenimoto, Yuki
%Y Mohapatra, Biswesh
%Y Stricker, Armand
%Y Khosla, Sopan
%S Proceedings of the 20th Workshop of Young Researchers’ Roundtable on Spoken Dialogue Systems
%D 2024
%8 September
%I Association for Computational Linguistics
%C Kyoto, Japan
%F yoshida-2024-personalized
%X In our research, we aim to achieve SDS capable of generating responses considering user preferences. While users have individual topic preferences, existing SDSs do not adequately consider such information. With the development of LLMs, SDSs are expected to be implemented in various tasks, including coexisting with humans in robotic applications. To become better partners with humans, systems are anticipated to memorize user preferences and utilize them in their response generation. Our future reserarch aim to realize SDSs that can remember and complement user information through dialogue, enabling personalized interactions. In YRRSDS, The author would like to propose the following topics for discussion. 1. What is the necessity of SDSs aimed specifically at dialogue rather than being just user interfaces? What do general users need from SDSs through conversation? 2. The relationship between SDSs and users: Should SDSs act just as agents, or should they aim to become like friends or family? 3. Privacy in conversational content. Nowadays, many SDS applications operate online via APIs, but is this preferable from a privacy perspective? If it is not preferable, how can this issue be resolved?
%U https://aclanthology.org/2024.yrrsds-1.5
%P 14-15

Markdown (Informal)

[Personalized Topic Transition for Dialogue System](https://aclanthology.org/2024.yrrsds-1.5) (Yoshida, YRRSDS-WS 2024)

• Personalized Topic Transition for Dialogue System (Yoshida, YRRSDS-WS 2024)

ACL

• Kai Yoshida. 2024. Personalized Topic Transition for Dialogue System. In Proceedings of the 20th Workshop of Young Researchers' Roundtable on Spoken Dialogue Systems, pages 14–15, Kyoto, Japan. Association for Computational Linguistics.