<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2020-learning-multi">
    <titleInfo>
        <title>Learning a Multi-Domain Curriculum for Neural Machine Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Wei</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ye</namePart>
        <namePart type="family">Tian</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jiquan</namePart>
        <namePart type="family">Ngiam</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yinfei</namePart>
        <namePart type="family">Yang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Isaac</namePart>
        <namePart type="family">Caswell</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zarana</namePart>
        <namePart type="family">Parekh</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 58th Annual Meeting of the Association for Computational Linguistics</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>Most data selection research in machine translation focuses on improving a single domain. We perform data selection for multiple domains at once. This is achieved by carefully introducing instance-level domain-relevance features and automatically constructing a training curriculum to gradually concentrate on multi-domain relevant and noise-reduced data batches. Both the choice of features and the use of curriculum are crucial for balancing and improving all domains, including out-of-domain. In large-scale experiments, the multi-domain curriculum simultaneously reaches or outperforms the individual performance and brings solid gains over no-curriculum training.</abstract>
    <identifier type="citekey">wang-etal-2020-learning-multi</identifier>
    <identifier type="doi">10.18653/v1/2020.acl-main.689</identifier>
    <location>
        <url>https://aclanthology.org/2020.acl-main.689</url>
    </location>
    <part>
        <date>2020-jul</date>
        <extent unit="page">
            <start>7711</start>
            <end>7723</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Learning a Multi-Domain Curriculum for Neural Machine Translation
%A Wang, Wei
%A Tian, Ye
%A Ngiam, Jiquan
%A Yang, Yinfei
%A Caswell, Isaac
%A Parekh, Zarana
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 jul
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-learning-multi
%X Most data selection research in machine translation focuses on improving a single domain. We perform data selection for multiple domains at once. This is achieved by carefully introducing instance-level domain-relevance features and automatically constructing a training curriculum to gradually concentrate on multi-domain relevant and noise-reduced data batches. Both the choice of features and the use of curriculum are crucial for balancing and improving all domains, including out-of-domain. In large-scale experiments, the multi-domain curriculum simultaneously reaches or outperforms the individual performance and brings solid gains over no-curriculum training.
%R 10.18653/v1/2020.acl-main.689
%U https://aclanthology.org/2020.acl-main.689
%U https://doi.org/10.18653/v1/2020.acl-main.689
%P 7711-7723

Markdown (Informal)

[Learning a Multi-Domain Curriculum for Neural Machine Translation](https://aclanthology.org/2020.acl-main.689) (Wang et al., ACL 2020)

• Learning a Multi-Domain Curriculum for Neural Machine Translation (Wang et al., ACL 2020)

ACL

• Wei Wang, Ye Tian, Jiquan Ngiam, Yinfei Yang, Isaac Caswell, and Zarana Parekh. 2020. Learning a Multi-Domain Curriculum for Neural Machine Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7711–7723, Online. Association for Computational Linguistics.