Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains

Vilém Zouhar, Shuoyang Ding, Anna Currey, Tatyana Badeka, Jenyuan Wang, Brian Thompson


Abstract
We introduce a new, extensive multidimensional quality metrics (MQM) annotated dataset covering 11 language pairs in the biomedical domain. We use this dataset to investigate whether machine translation (MT) metrics which are fine-tuned on human-generated MT quality judgements are robust to domain shifts between training and inference. We find that fine-tuned metrics exhibit a substantial performance drop in the unseen domain scenario relative to both metrics that rely on the surface form and pre-trained metrics that are not fine-tuned on MT quality judgments.
Anthology ID:
2024.acl-short.45
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
488–500
Language:
URL:
https://aclanthology.org/2024.acl-short.45
DOI:
10.18653/v1/2024.acl-short.45
Bibkey:
Cite (ACL):
Vilém Zouhar, Shuoyang Ding, Anna Currey, Tatyana Badeka, Jenyuan Wang, and Brian Thompson. 2024. Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 488–500, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains (Zouhar et al., ACL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.acl-short.45.pdf