Open-Domain Dialog Evaluation Using Follow-Ups Likelihood

Maxime De Bruyn; Ehsan Lotfi; Jeska Buhmann; Walter Daelemans

Open-Domain Dialog Evaluation Using Follow-Ups Likelihood

Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann, Walter Daelemans

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Abstract

Automatic evaluation of open-domain dialogs remains an unsolved problem. Existing methods do not correlate strongly with human annotations. In this paper, we present a new automated evaluation method based on the use of follow-ups. We measure the probability that a language model will continue the conversation with a fixed set of follow-ups (e.g. not really relevant here, what are you trying to say?). When compared against twelve existing methods, our new evaluation achieves the highest correlation with human evaluations.

Anthology ID:: 2022.coling-1.40
Volume:: Proceedings of the 29th International Conference on Computational Linguistics
Month:: October
Year:: 2022
Address:: Gyeongju, Republic of Korea
Editors:: Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
Venue:: COLING
SIG:
Publisher:: International Committee on Computational Linguistics
Note:
Pages:: 496–504
Language:
URL:: https://aclanthology.org/2022.coling-1.40/
DOI:
Bibkey:
Cite (ACL):: Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann, and Walter Daelemans. 2022. Open-Domain Dialog Evaluation Using Follow-Ups Likelihood. In Proceedings of the 29th International Conference on Computational Linguistics, pages 496–504, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):: Open-Domain Dialog Evaluation Using Follow-Ups Likelihood (De Bruyn et al., COLING 2022)
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PDF:: https://aclanthology.org/2022.coling-1.40.pdf

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@inproceedings{de-bruyn-etal-2022-open,
    title = "Open-Domain Dialog Evaluation Using Follow-Ups Likelihood",
    author = "De Bruyn, Maxime  and
      Lotfi, Ehsan  and
      Buhmann, Jeska  and
      Daelemans, Walter",
    editor = "Calzolari, Nicoletta  and
      Huang, Chu-Ren  and
      Kim, Hansaem  and
      Pustejovsky, James  and
      Wanner, Leo  and
      Choi, Key-Sun  and
      Ryu, Pum-Mo  and
      Chen, Hsin-Hsi  and
      Donatelli, Lucia  and
      Ji, Heng  and
      Kurohashi, Sadao  and
      Paggio, Patrizia  and
      Xue, Nianwen  and
      Kim, Seokhwan  and
      Hahm, Younggyun  and
      He, Zhong  and
      Lee, Tony Kyungil  and
      Santus, Enrico  and
      Bond, Francis  and
      Na, Seung-Hoon",
    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2022.coling-1.40/",
    pages = "496--504",
    abstract = "Automatic evaluation of open-domain dialogs remains an unsolved problem. Existing methods do not correlate strongly with human annotations. In this paper, we present a new automated evaluation method based on the use of follow-ups. We measure the probability that a language model will continue the conversation with a fixed set of follow-ups (e.g. not really relevant here, what are you trying to say?). When compared against twelve existing methods, our new evaluation achieves the highest correlation with human evaluations."
}

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%0 Conference Proceedings
%T Open-Domain Dialog Evaluation Using Follow-Ups Likelihood
%A De Bruyn, Maxime
%A Lotfi, Ehsan
%A Buhmann, Jeska
%A Daelemans, Walter
%Y Calzolari, Nicoletta
%Y Huang, Chu-Ren
%Y Kim, Hansaem
%Y Pustejovsky, James
%Y Wanner, Leo
%Y Choi, Key-Sun
%Y Ryu, Pum-Mo
%Y Chen, Hsin-Hsi
%Y Donatelli, Lucia
%Y Ji, Heng
%Y Kurohashi, Sadao
%Y Paggio, Patrizia
%Y Xue, Nianwen
%Y Kim, Seokhwan
%Y Hahm, Younggyun
%Y He, Zhong
%Y Lee, Tony Kyungil
%Y Santus, Enrico
%Y Bond, Francis
%Y Na, Seung-Hoon
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F de-bruyn-etal-2022-open
%X Automatic evaluation of open-domain dialogs remains an unsolved problem. Existing methods do not correlate strongly with human annotations. In this paper, we present a new automated evaluation method based on the use of follow-ups. We measure the probability that a language model will continue the conversation with a fixed set of follow-ups (e.g. not really relevant here, what are you trying to say?). When compared against twelve existing methods, our new evaluation achieves the highest correlation with human evaluations.
%U https://aclanthology.org/2022.coling-1.40/
%P 496-504

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Markdown (Informal)

[Open-Domain Dialog Evaluation Using Follow-Ups Likelihood](https://aclanthology.org/2022.coling-1.40/) (De Bruyn et al., COLING 2022)

Open-Domain Dialog Evaluation Using Follow-Ups Likelihood (De Bruyn et al., COLING 2022)

ACL

Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann, and Walter Daelemans. 2022. Open-Domain Dialog Evaluation Using Follow-Ups Likelihood. In Proceedings of the 29th International Conference on Computational Linguistics, pages 496–504, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.