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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2022.coling-1.40.pdf
- Code
- maximedb/full
- Data
- FED