MSLC25: Metric Performance on Low-Quality Machine Translation, Empty Strings, and Language Variants

Rebecca Knowles, Samuel Larkin, Chi-Kiu Lo


Abstract
In this challenge set, we examine how automatic metrics for machine translation perform on a wide variety of machine translation output, covering a wider range of quality than the WMT submissions. We also explore metric results on specific types of corner cases, such as empty strings, wrong- or mixed-language text, and more. We primarily focus on Japanese–Chinese data, with some work on English and Czech.
Anthology ID:
2025.wmt-1.69
Volume:
Proceedings of the Tenth Conference on Machine Translation
Month:
November
Year:
2025
Address:
Suzhou, China
Editors:
Barry Haddow, Tom Kocmi, Philipp Koehn, Christof Monz
Venue:
WMT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
945–956
Language:
URL:
https://preview.aclanthology.org/ingest-emnlp/2025.wmt-1.69/
DOI:
Bibkey:
Cite (ACL):
Rebecca Knowles, Samuel Larkin, and Chi-Kiu Lo. 2025. MSLC25: Metric Performance on Low-Quality Machine Translation, Empty Strings, and Language Variants. In Proceedings of the Tenth Conference on Machine Translation, pages 945–956, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
MSLC25: Metric Performance on Low-Quality Machine Translation, Empty Strings, and Language Variants (Knowles et al., WMT 2025)
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PDF:
https://preview.aclanthology.org/ingest-emnlp/2025.wmt-1.69.pdf