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        <title>Investigating person-specific errors in chat-oriented dialogue systems</title>
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    <name type="personal">
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    <abstract>Creating chatbots to behave like real people is important in terms of believability. Errors in general chatbots and chatbots that follow a rough persona have been studied, but those in chatbots that behave like real people have not been thoroughly investigated. We collected a large amount of user interactions of a generation-based chatbot trained from large-scale dialogue data of a specific character, i.e., target person, and analyzed errors related to that person. We found that person-specific errors can be divided into two types: errors in attributes and those in relations, each of which can be divided into two levels: self and other. The correspondence with an existing taxonomy of errors was also investigated, and person-specific errors that should be addressed in the future were clarified.</abstract>
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%0 Conference Proceedings
%T Investigating person-specific errors in chat-oriented dialogue systems
%A Mitsuda, Koh
%A Higashinaka, Ryuichiro
%A Li, Tingxuan
%A Yoshida, Sen
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F mitsuda-etal-2022-investigating
%X Creating chatbots to behave like real people is important in terms of believability. Errors in general chatbots and chatbots that follow a rough persona have been studied, but those in chatbots that behave like real people have not been thoroughly investigated. We collected a large amount of user interactions of a generation-based chatbot trained from large-scale dialogue data of a specific character, i.e., target person, and analyzed errors related to that person. We found that person-specific errors can be divided into two types: errors in attributes and those in relations, each of which can be divided into two levels: self and other. The correspondence with an existing taxonomy of errors was also investigated, and person-specific errors that should be addressed in the future were clarified.
%R 10.18653/v1/2022.acl-short.50
%U https://aclanthology.org/2022.acl-short.50
%U https://doi.org/10.18653/v1/2022.acl-short.50
%P 464-469

Markdown (Informal)

[Investigating person-specific errors in chat-oriented dialogue systems](https://aclanthology.org/2022.acl-short.50) (Mitsuda et al., ACL 2022)

• Investigating person-specific errors in chat-oriented dialogue systems (Mitsuda et al., ACL 2022)

ACL

• Koh Mitsuda, Ryuichiro Higashinaka, Tingxuan Li, and Sen Yoshida. 2022. Investigating person-specific errors in chat-oriented dialogue systems. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 464–469, Dublin, Ireland. Association for Computational Linguistics.