<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="haddow-etal-2022-survey">
    <titleInfo>
        <title>Survey of Low-Resource Machine Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Barry</namePart>
        <namePart type="family">Haddow</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Rachel</namePart>
        <namePart type="family">Bawden</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Antonio</namePart>
        <namePart type="given">Valerio</namePart>
        <namePart type="family">Miceli Barone</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jindřich</namePart>
        <namePart type="family">Helcl</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Alexandra</namePart>
        <namePart type="family">Birch</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>We present a survey covering the state of the art in low-resource machine translation (MT) research. There are currently around 7,000 languages spoken in the world and almost all language pairs lack significant resources for training machine translation models. There has been increasing interest in research addressing the challenge of producing useful translation models when very little translated training data is available. We present a summary of this topical research field and provide a description of the techniques evaluated by researchers in several recent shared tasks in low-resource MT.</abstract>
    <identifier type="citekey">haddow-etal-2022-survey</identifier>
    <identifier type="doi">10.1162/coli_a_00446</identifier>
    <location>
        <url>https://aclanthology.org/2022.cl-3.6</url>
    </location>
    <part>
        <date>2022-09</date>
        <detail type="volume"><number>48</number></detail>
        <detail type="issue"><number>3</number></detail>
        <extent unit="page">
            <start>673</start>
            <end>732</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Survey of Low-Resource Machine Translation
%A Haddow, Barry
%A Bawden, Rachel
%A Miceli Barone, Antonio Valerio
%A Helcl, Jindřich
%A Birch, Alexandra
%J Computational Linguistics
%D 2022
%8 September
%V 48
%N 3
%I MIT Press
%C Cambridge, MA
%F haddow-etal-2022-survey
%X We present a survey covering the state of the art in low-resource machine translation (MT) research. There are currently around 7,000 languages spoken in the world and almost all language pairs lack significant resources for training machine translation models. There has been increasing interest in research addressing the challenge of producing useful translation models when very little translated training data is available. We present a summary of this topical research field and provide a description of the techniques evaluated by researchers in several recent shared tasks in low-resource MT.
%R 10.1162/coli_a_00446
%U https://aclanthology.org/2022.cl-3.6
%U https://doi.org/10.1162/coli_a_00446
%P 673-732

Markdown (Informal)

[Survey of Low-Resource Machine Translation](https://aclanthology.org/2022.cl-3.6) (Haddow et al., CL 2022)

• Survey of Low-Resource Machine Translation (Haddow et al., CL 2022)

ACL

• Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, Jindřich Helcl, and Alexandra Birch. 2022. Survey of Low-Resource Machine Translation. Computational Linguistics, 48(3):673–732.