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        <title>MUNI-NLP Submission for Czech-Ukrainian Translation Task at WMT23</title>
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    <name type="personal">
        <namePart type="given">Pavel</namePart>
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            <title>Proceedings of the Eighth Conference on Machine Translation</title>
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        <name type="personal">
            <namePart type="given">Philipp</namePart>
            <namePart type="family">Koehn</namePart>
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        <name type="personal">
            <namePart type="given">Barry</namePart>
            <namePart type="family">Haddow</namePart>
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        <name type="personal">
            <namePart type="given">Tom</namePart>
            <namePart type="family">Kocmi</namePart>
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        <name type="personal">
            <namePart type="given">Christof</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>The system is trained on officialy provided data only. We have heavily filtered all the data to remove machine translated text, Russian text and other noise. We use the DeepNorm modification of the transformer architecture in the TorchScale library with 18 encoder layers and 6 decoder layers. The initial systems for backtranslation uses HFT tokenizer, the final system uses custom tokenizer derived from HFT.</abstract>
    <identifier type="citekey">rychly-teslia-2023-muni</identifier>
    <identifier type="doi">10.18653/v1/2023.wmt-1.14</identifier>
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        <url>https://aclanthology.org/2023.wmt-1.14</url>
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%0 Conference Proceedings
%T MUNI-NLP Submission for Czech-Ukrainian Translation Task at WMT23
%A Rychly, Pavel
%A Teslia, Yuliia
%Y Koehn, Philipp
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Monz, Christof
%S Proceedings of the Eighth Conference on Machine Translation
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F rychly-teslia-2023-muni
%X The system is trained on officialy provided data only. We have heavily filtered all the data to remove machine translated text, Russian text and other noise. We use the DeepNorm modification of the transformer architecture in the TorchScale library with 18 encoder layers and 6 decoder layers. The initial systems for backtranslation uses HFT tokenizer, the final system uses custom tokenizer derived from HFT.
%R 10.18653/v1/2023.wmt-1.14
%U https://aclanthology.org/2023.wmt-1.14
%U https://doi.org/10.18653/v1/2023.wmt-1.14
%P 162-165

Markdown (Informal)

[MUNI-NLP Submission for Czech-Ukrainian Translation Task at WMT23](https://aclanthology.org/2023.wmt-1.14) (Rychly & Teslia, WMT 2023)

• MUNI-NLP Submission for Czech-Ukrainian Translation Task at WMT23 (Rychly & Teslia, WMT 2023)

ACL

• Pavel Rychly and Yuliia Teslia. 2023. MUNI-NLP Submission for Czech-Ukrainian Translation Task at WMT23. In Proceedings of the Eighth Conference on Machine Translation, pages 162–165, Singapore. Association for Computational Linguistics.