Open-Domain Dialog Evaluation Using Follow-Ups Likelihood
Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann, Walter Daelemans
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)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.40.pdf
- Code
- maximedb/full
- Data
- FED
Export citation
@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
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.