Pitfalls and Outlooks in Using COMET

Vilém Zouhar, Pinzhen Chen, Tsz Kin Lam, Nikita Moghe, Barry Haddow


Abstract
The COMET metric has blazed a trail in the machine translation community, given its strong correlation with human judgements of translation quality.Its success stems from being a modified pre-trained multilingual model finetuned for quality assessment.However, it being a machine learning model also gives rise to a new set of pitfalls that may not be widely known. We investigate these unexpected behaviours from three aspects:1) technical: obsolete software versions and compute precision; 2) data: empty content, language mismatch, and translationese at test time as well as distribution and domain biases in training; 3) usage and reporting: multi-reference support and model referencing in the literature. All of these problems imply that COMET scores are not comparable between papers or even technical setups and we put forward our perspective on fixing each issue.Furthermore, we release the sacreCOMET package that can generate a signature for the software and model configuration as well as an appropriate citation.The goal of this work is to help the community make more sound use of the COMET metric.
Anthology ID:
2024.wmt-1.121
Volume:
Proceedings of the Ninth Conference on Machine Translation
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Barry Haddow, Tom Kocmi, Philipp Koehn, Christof Monz
Venue:
WMT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1272–1288
Language:
URL:
https://aclanthology.org/2024.wmt-1.121
DOI:
10.18653/v1/2024.wmt-1.121
Bibkey:
Cite (ACL):
Vilém Zouhar, Pinzhen Chen, Tsz Kin Lam, Nikita Moghe, and Barry Haddow. 2024. Pitfalls and Outlooks in Using COMET. In Proceedings of the Ninth Conference on Machine Translation, pages 1272–1288, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Pitfalls and Outlooks in Using COMET (Zouhar et al., WMT 2024)
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PDF:
https://aclanthology.org/2024.wmt-1.121.pdf