<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2021-tencent-ai">
    <titleInfo>
        <title>Tencent AI Lab Machine Translation Systems for the WMT21 Biomedical Translation Task</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Xing</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhaopeng</namePart>
        <namePart type="family">Tu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shuming</namePart>
        <namePart type="family">Shi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-nov</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Sixth Conference on Machine Translation</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper describes the Tencent AI Lab submission of the WMT2021 shared task on biomedical translation in eight language directions: English-German, English-French, English-Spanish and English-Russian. We utilized different Transformer architectures, pretraining and back-translation strategies to improve translation quality. Concretely, we explore mBART (Liu et al., 2020) to demonstrate the effectiveness of the pretraining strategy. Our submissions (Tencent AI Lab Machine Translation, TMT) in German/French/Spanish⇒English are ranked 1st respectively according to the official evaluation results in terms of BLEU scores.</abstract>
    <identifier type="citekey">wang-etal-2021-tencent-ai</identifier>
    <location>
        <url>https://aclanthology.org/2021.wmt-1.89</url>
    </location>
    <part>
        <date>2021-nov</date>
        <extent unit="page">
            <start>874</start>
            <end>878</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Tencent AI Lab Machine Translation Systems for the WMT21 Biomedical Translation Task
%A Wang, Xing
%A Tu, Zhaopeng
%A Shi, Shuming
%S Proceedings of the Sixth Conference on Machine Translation
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Online
%F wang-etal-2021-tencent-ai
%X This paper describes the Tencent AI Lab submission of the WMT2021 shared task on biomedical translation in eight language directions: English-German, English-French, English-Spanish and English-Russian. We utilized different Transformer architectures, pretraining and back-translation strategies to improve translation quality. Concretely, we explore mBART (Liu et al., 2020) to demonstrate the effectiveness of the pretraining strategy. Our submissions (Tencent AI Lab Machine Translation, TMT) in German/French/Spanish⇒English are ranked 1st respectively according to the official evaluation results in terms of BLEU scores.
%U https://aclanthology.org/2021.wmt-1.89
%P 874-878

Markdown (Informal)

[Tencent AI Lab Machine Translation Systems for the WMT21 Biomedical Translation Task](https://aclanthology.org/2021.wmt-1.89) (Wang et al., WMT 2021)

• Tencent AI Lab Machine Translation Systems for the WMT21 Biomedical Translation Task (Wang et al., WMT 2021)

ACL

• Xing Wang, Zhaopeng Tu, and Shuming Shi. 2021. Tencent AI Lab Machine Translation Systems for the WMT21 Biomedical Translation Task. In Proceedings of the Sixth Conference on Machine Translation, pages 874–878, Online. Association for Computational Linguistics.