@inproceedings{tsunomori-higashinaka-2024-remember-sui,
title = "{I} Remember You!: {SUI} Corpus for Remembering and Utilizing Users{'} Information in Chat-oriented Dialogue Systems",
author = "Tsunomori, Yuiko and
Higashinaka, Ryuichiro",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.811",
pages = "9285--9295",
abstract = "To construct a chat-oriented dialogue system that will be used for a long time by users, it is important to build a good relationship between the user and the system. To achieve a good relationship, several methods for remembering and utilizing information on users (preferences, experiences, jobs, etc.) in system utterances have been investigated. One way to do this is to utilize user information to fill in utterance templates for use in response generation, but the utterances do not always fit the context. Another way is to use neural-based generation, but in current methods, user information can be incorporated only when the current dialogue topic is similar to that of the user information. This paper tackled these problems by constructing a novel corpus to incorporate arbitrary user information into system utterances regardless of the current dialogue topic while retaining appropriateness for the context. We then fine-tuned a model for generating system utterances using the constructed corpus. The result of a subjective evaluation demonstrated the effectiveness of our model. Furthermore, we incorporated our fine-tuned model into a dialogue system and confirmed the effectiveness of the system through interactive dialogues with users.",
}
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<abstract>To construct a chat-oriented dialogue system that will be used for a long time by users, it is important to build a good relationship between the user and the system. To achieve a good relationship, several methods for remembering and utilizing information on users (preferences, experiences, jobs, etc.) in system utterances have been investigated. One way to do this is to utilize user information to fill in utterance templates for use in response generation, but the utterances do not always fit the context. Another way is to use neural-based generation, but in current methods, user information can be incorporated only when the current dialogue topic is similar to that of the user information. This paper tackled these problems by constructing a novel corpus to incorporate arbitrary user information into system utterances regardless of the current dialogue topic while retaining appropriateness for the context. We then fine-tuned a model for generating system utterances using the constructed corpus. The result of a subjective evaluation demonstrated the effectiveness of our model. Furthermore, we incorporated our fine-tuned model into a dialogue system and confirmed the effectiveness of the system through interactive dialogues with users.</abstract>
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%0 Conference Proceedings
%T I Remember You!: SUI Corpus for Remembering and Utilizing Users’ Information in Chat-oriented Dialogue Systems
%A Tsunomori, Yuiko
%A Higashinaka, Ryuichiro
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F tsunomori-higashinaka-2024-remember-sui
%X To construct a chat-oriented dialogue system that will be used for a long time by users, it is important to build a good relationship between the user and the system. To achieve a good relationship, several methods for remembering and utilizing information on users (preferences, experiences, jobs, etc.) in system utterances have been investigated. One way to do this is to utilize user information to fill in utterance templates for use in response generation, but the utterances do not always fit the context. Another way is to use neural-based generation, but in current methods, user information can be incorporated only when the current dialogue topic is similar to that of the user information. This paper tackled these problems by constructing a novel corpus to incorporate arbitrary user information into system utterances regardless of the current dialogue topic while retaining appropriateness for the context. We then fine-tuned a model for generating system utterances using the constructed corpus. The result of a subjective evaluation demonstrated the effectiveness of our model. Furthermore, we incorporated our fine-tuned model into a dialogue system and confirmed the effectiveness of the system through interactive dialogues with users.
%U https://aclanthology.org/2024.lrec-main.811
%P 9285-9295
Markdown (Informal)
[I Remember You!: SUI Corpus for Remembering and Utilizing Users’ Information in Chat-oriented Dialogue Systems](https://aclanthology.org/2024.lrec-main.811) (Tsunomori & Higashinaka, LREC-COLING 2024)
ACL