<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="abbott-martinus-2019-benchmarking">
    <titleInfo>
        <title>Benchmarking Neural Machine Translation for Southern African Languages</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jade</namePart>
        <namePart type="family">Abbott</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Laura</namePart>
        <namePart type="family">Martinus</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-aug</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2019 Workshop on Widening NLP</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Unlike major Western languages, most African languages are very low-resourced. Furthermore, the resources that do exist are often scattered and difficult to obtain and discover. As a result, the data and code for existing research has rarely been shared, meaning researchers struggle to reproduce reported results, and almost no publicly available benchmarks or leaderboards for African machine translation models exist. To start to address these problems, we trained neural machine translation models for a subset of Southern African languages on publicly-available datasets. We provide the code for training the models and evaluate the models on a newly released evaluation set, with the aim of starting a leaderboard for Southern African languages and spur future research in the field.</abstract>
    <identifier type="citekey">abbott-martinus-2019-benchmarking</identifier>
    <location>
        <url>https://aclanthology.org/W19-3632</url>
    </location>
    <part>
        <date>2019-aug</date>
        <extent unit="page">
            <start>98</start>
            <end>101</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Benchmarking Neural Machine Translation for Southern African Languages
%A Abbott, Jade
%A Martinus, Laura
%S Proceedings of the 2019 Workshop on Widening NLP
%D 2019
%8 aug
%I Association for Computational Linguistics
%C Florence, Italy
%F abbott-martinus-2019-benchmarking
%X Unlike major Western languages, most African languages are very low-resourced. Furthermore, the resources that do exist are often scattered and difficult to obtain and discover. As a result, the data and code for existing research has rarely been shared, meaning researchers struggle to reproduce reported results, and almost no publicly available benchmarks or leaderboards for African machine translation models exist. To start to address these problems, we trained neural machine translation models for a subset of Southern African languages on publicly-available datasets. We provide the code for training the models and evaluate the models on a newly released evaluation set, with the aim of starting a leaderboard for Southern African languages and spur future research in the field.
%U https://aclanthology.org/W19-3632
%P 98-101

Markdown (Informal)

[Benchmarking Neural Machine Translation for Southern African Languages](https://aclanthology.org/W19-3632) (Abbott & Martinus, 2019)

• Benchmarking Neural Machine Translation for Southern African Languages (Abbott & Martinus, 2019)

ACL

• Jade Abbott and Laura Martinus. 2019. Benchmarking Neural Machine Translation for Southern African Languages. In Proceedings of the 2019 Workshop on Widening NLP, pages 98–101, Florence, Italy. Association for Computational Linguistics.