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        <title>CUNI Systems for WMT21: Terminology Translation Shared Task</title>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Michal</namePart>
        <namePart type="family">Novák</namePart>
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    <name type="personal">
        <namePart type="given">João</namePart>
        <namePart type="given">Paulo</namePart>
        <namePart type="family">Aires</namePart>
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    <name type="personal">
        <namePart type="given">Dusan</namePart>
        <namePart type="family">Varis</namePart>
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    <name type="personal">
        <namePart type="given">Ondřej</namePart>
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    <abstract>This paper describes Charles University sub-mission for Terminology translation Shared Task at WMT21. The objective of this task is to design a system which translates certain terms based on a provided terminology database, while preserving high overall translation quality. We competed in English-French language pair. Our approach is based on providing the desired translations alongside the input sentence and training the model to use these provided terms. We lemmatize the terms both during the training and inference, to allow the model to learn how to produce correct surface forms of the words, when they differ from the forms provided in the terminology database. Our submission ranked second in Exact Match metric which evaluates the ability of the model to produce desired terms in the translation.</abstract>
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        <url>https://aclanthology.org/2021.wmt-1.82</url>
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        <date>2021-nov</date>
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%0 Conference Proceedings
%T CUNI Systems for WMT21: Terminology Translation Shared Task
%A Jon, Josef
%A Novák, Michal
%A Aires, João Paulo
%A Varis, Dusan
%A Bojar, Ondřej
%S Proceedings of the Sixth Conference on Machine Translation
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Online
%F jon-etal-2021-cuni-systems
%X This paper describes Charles University sub-mission for Terminology translation Shared Task at WMT21. The objective of this task is to design a system which translates certain terms based on a provided terminology database, while preserving high overall translation quality. We competed in English-French language pair. Our approach is based on providing the desired translations alongside the input sentence and training the model to use these provided terms. We lemmatize the terms both during the training and inference, to allow the model to learn how to produce correct surface forms of the words, when they differ from the forms provided in the terminology database. Our submission ranked second in Exact Match metric which evaluates the ability of the model to produce desired terms in the translation.
%U https://aclanthology.org/2021.wmt-1.82
%P 828-834

Markdown (Informal)

[CUNI Systems for WMT21: Terminology Translation Shared Task](https://aclanthology.org/2021.wmt-1.82) (Jon et al., WMT 2021)

• CUNI Systems for WMT21: Terminology Translation Shared Task (Jon et al., WMT 2021)

ACL

• Josef Jon, Michal Novák, João Paulo Aires, Dusan Varis, and Ondřej Bojar. 2021. CUNI Systems for WMT21: Terminology Translation Shared Task. In Proceedings of the Sixth Conference on Machine Translation, pages 828–834, Online. Association for Computational Linguistics.