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        <title>MSLC24 Submissions to the General Machine Translation Task</title>
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        <namePart type="given">Samuel</namePart>
        <namePart type="family">Larkin</namePart>
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    <name type="personal">
        <namePart type="given">Chi-Kiu</namePart>
        <namePart type="family">Lo</namePart>
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    <name type="personal">
        <namePart type="given">Rebecca</namePart>
        <namePart type="family">Knowles</namePart>
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        <dateIssued>2024-11</dateIssued>
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            <title>Proceedings of the Ninth Conference on Machine Translation</title>
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            <namePart type="given">Barry</namePart>
            <namePart type="family">Haddow</namePart>
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        <name type="personal">
            <namePart type="given">Tom</namePart>
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        <name type="personal">
            <namePart type="given">Philipp</namePart>
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        <name type="personal">
            <namePart type="given">Christof</namePart>
            <namePart type="family">Monz</namePart>
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    <abstract>The MSLC (Metric Score Landscape Challenge) submissions for English-German, English-Spanish, and Japanese-Chinese are constrained systems built using Transformer models for the purpose of better evaluating metric performance in the WMT24 Metrics Task. They are intended to be representative of the performance of systems that can be built relatively simply using constrained data and with minimal modifications to the translation training pipeline.</abstract>
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        <url>https://aclanthology.org/2024.wmt-1.7</url>
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%0 Conference Proceedings
%T MSLC24 Submissions to the General Machine Translation Task
%A Larkin, Samuel
%A Lo, Chi-Kiu
%A Knowles, Rebecca
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F larkin-etal-2024-mslc24
%X The MSLC (Metric Score Landscape Challenge) submissions for English-German, English-Spanish, and Japanese-Chinese are constrained systems built using Transformer models for the purpose of better evaluating metric performance in the WMT24 Metrics Task. They are intended to be representative of the performance of systems that can be built relatively simply using constrained data and with minimal modifications to the translation training pipeline.
%U https://aclanthology.org/2024.wmt-1.7
%P 139-146

Markdown (Informal)

[MSLC24 Submissions to the General Machine Translation Task](https://aclanthology.org/2024.wmt-1.7) (Larkin et al., WMT 2024)

• MSLC24 Submissions to the General Machine Translation Task (Larkin et al., WMT 2024)

ACL

• Samuel Larkin, Chi-Kiu Lo, and Rebecca Knowles. 2024. MSLC24 Submissions to the General Machine Translation Task. In Proceedings of the Ninth Conference on Machine Translation, pages 139–146, Miami, Florida, USA. Association for Computational Linguistics.