<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gowda-etal-2021-many">
    <titleInfo>
        <title>Many-to-English Machine Translation Tools, Data, and Pretrained Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Thamme</namePart>
        <namePart type="family">Gowda</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhao</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chris</namePart>
        <namePart type="family">Mattmann</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jonathan</namePart>
        <namePart type="family">May</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Heng</namePart>
            <namePart type="family">Ji</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jong</namePart>
            <namePart type="given">C</namePart>
            <namePart type="family">Park</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Rui</namePart>
            <namePart type="family">Xia</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>While there are more than 7000 languages in the world, most translation research efforts have targeted a few high resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTData, NLCodec and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages.</abstract>
    <identifier type="citekey">gowda-etal-2021-many</identifier>
    <identifier type="doi">10.18653/v1/2021.acl-demo.37</identifier>
    <location>
        <url>https://aclanthology.org/2021.acl-demo.37/</url>
    </location>
    <part>
        <date>2021-08</date>
        <extent unit="page">
            <start>306</start>
            <end>316</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Many-to-English Machine Translation Tools, Data, and Pretrained Models
%A Gowda, Thamme
%A Zhang, Zhao
%A Mattmann, Chris
%A May, Jonathan
%Y Ji, Heng
%Y Park, Jong C.
%Y Xia, Rui
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F gowda-etal-2021-many
%X While there are more than 7000 languages in the world, most translation research efforts have targeted a few high resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTData, NLCodec and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages.
%R 10.18653/v1/2021.acl-demo.37
%U https://aclanthology.org/2021.acl-demo.37/
%U https://doi.org/10.18653/v1/2021.acl-demo.37
%P 306-316

Markdown (Informal)

[Many-to-English Machine Translation Tools, Data, and Pretrained Models](https://aclanthology.org/2021.acl-demo.37/) (Gowda et al., ACL-IJCNLP 2021)

• Many-to-English Machine Translation Tools, Data, and Pretrained Models (Gowda et al., ACL-IJCNLP 2021)

ACL

• Thamme Gowda, Zhao Zhang, Chris Mattmann, and Jonathan May. 2021. Many-to-English Machine Translation Tools, Data, and Pretrained Models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 306–316, Online. Association for Computational Linguistics.