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<mods ID="agrawal-carpuat-2020-multitask">
    <titleInfo>
        <title>Multitask Models for Controlling the Complexity of Neural Machine Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sweta</namePart>
        <namePart type="family">Agrawal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Marine</namePart>
        <namePart type="family">Carpuat</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-jul</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the The Fourth Widening Natural Language Processing Workshop</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Seattle, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We introduce a machine translation task where the output is aimed at audiences of different levels of target language proficiency. We collect a novel dataset of news articles available in English and Spanish and written for diverse reading grade levels. We leverage this dataset to train multitask sequence to sequence models that translate Spanish into English targeted at an easier reading grade level than the original Spanish. We show that multitask models outperform pipeline approaches that translate and simplify text independently.</abstract>
    <identifier type="citekey">agrawal-carpuat-2020-multitask</identifier>
    <part>
        <date>2020-jul</date>
        <extent unit="page">
            <start>136</start>
            <end>139</end>
        </extent>
    </part>
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