@inproceedings{higashinaka-etal-2021-integrated,
title = "Integrated taxonomy of errors in chat-oriented dialogue systems",
author = "Higashinaka, Ryuichiro and
Araki, Masahiro and
Tsukahara, Hiroshi and
Mizukami, Masahiro",
editor = "Li, Haizhou and
Levow, Gina-Anne and
Yu, Zhou and
Gupta, Chitralekha and
Sisman, Berrak and
Cai, Siqi and
Vandyke, David and
Dethlefs, Nina and
Wu, Yan and
Li, Junyi Jessy",
booktitle = "Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = jul,
year = "2021",
address = "Singapore and Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sigdial-1.10/",
doi = "10.18653/v1/2021.sigdial-1.10",
pages = "89--98",
abstract = "This paper proposes a taxonomy of errors in chat-oriented dialogue systems. Previously, two taxonomies were proposed; one is theory-driven and the other data-driven. The former suffers from the fact that dialogue theories for human conversation are often not appropriate for categorizing errors made by chat-oriented dialogue systems. The latter has limitations in that it can only cope with errors of systems for which we have data. This paper integrates these two taxonomies to create a comprehensive taxonomy of errors in chat-oriented dialogue systems. We found that, with our integrated taxonomy, errors can be reliably annotated with a higher Fleiss' kappa compared with the previously proposed taxonomies."
}
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<abstract>This paper proposes a taxonomy of errors in chat-oriented dialogue systems. Previously, two taxonomies were proposed; one is theory-driven and the other data-driven. The former suffers from the fact that dialogue theories for human conversation are often not appropriate for categorizing errors made by chat-oriented dialogue systems. The latter has limitations in that it can only cope with errors of systems for which we have data. This paper integrates these two taxonomies to create a comprehensive taxonomy of errors in chat-oriented dialogue systems. We found that, with our integrated taxonomy, errors can be reliably annotated with a higher Fleiss’ kappa compared with the previously proposed taxonomies.</abstract>
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%0 Conference Proceedings
%T Integrated taxonomy of errors in chat-oriented dialogue systems
%A Higashinaka, Ryuichiro
%A Araki, Masahiro
%A Tsukahara, Hiroshi
%A Mizukami, Masahiro
%Y Li, Haizhou
%Y Levow, Gina-Anne
%Y Yu, Zhou
%Y Gupta, Chitralekha
%Y Sisman, Berrak
%Y Cai, Siqi
%Y Vandyke, David
%Y Dethlefs, Nina
%Y Wu, Yan
%Y Li, Junyi Jessy
%S Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2021
%8 July
%I Association for Computational Linguistics
%C Singapore and Online
%F higashinaka-etal-2021-integrated
%X This paper proposes a taxonomy of errors in chat-oriented dialogue systems. Previously, two taxonomies were proposed; one is theory-driven and the other data-driven. The former suffers from the fact that dialogue theories for human conversation are often not appropriate for categorizing errors made by chat-oriented dialogue systems. The latter has limitations in that it can only cope with errors of systems for which we have data. This paper integrates these two taxonomies to create a comprehensive taxonomy of errors in chat-oriented dialogue systems. We found that, with our integrated taxonomy, errors can be reliably annotated with a higher Fleiss’ kappa compared with the previously proposed taxonomies.
%R 10.18653/v1/2021.sigdial-1.10
%U https://aclanthology.org/2021.sigdial-1.10/
%U https://doi.org/10.18653/v1/2021.sigdial-1.10
%P 89-98
Markdown (Informal)
[Integrated taxonomy of errors in chat-oriented dialogue systems](https://aclanthology.org/2021.sigdial-1.10/) (Higashinaka et al., SIGDIAL 2021)
ACL
- Ryuichiro Higashinaka, Masahiro Araki, Hiroshi Tsukahara, and Masahiro Mizukami. 2021. Integrated taxonomy of errors in chat-oriented dialogue systems. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 89–98, Singapore and Online. Association for Computational Linguistics.