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            <title>Proceedings of the Tenth Conference on Machine Translation</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T MSLC25: Metric Performance on Low-Quality Machine Translation, Empty Strings, and Language Variants
%A Knowles, Rebecca
%A Larkin, Samuel
%A Lo, Chi-Kiu
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Tenth Conference on Machine Translation
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-341-8
%F knowles-etal-2025-mslc25
%X 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.
%U https://aclanthology.org/2025.wmt-1.69/
%P 945-956

Markdown (Informal)

[MSLC25: Metric Performance on Low-Quality Machine Translation, Empty Strings, and Language Variants](https://aclanthology.org/2025.wmt-1.69/) (Knowles et al., WMT 2025)

• MSLC25: Metric Performance on Low-Quality Machine Translation, Empty Strings, and Language Variants (Knowles et al., WMT 2025)

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.