<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="poncelas-etal-2018-adapt">
    <titleInfo>
        <title>The ADAPT System Description for the IWSLT 2018 Basque to English Translation Task</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Alberto</namePart>
        <namePart type="family">Poncelas</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Andy</namePart>
        <namePart type="family">Way</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kepa</namePart>
        <namePart type="family">Sarasola</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-oct 29-30</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 15th International Conference on Spoken Language Translation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Marco</namePart>
            <namePart type="family">Turchi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jan</namePart>
            <namePart type="family">Niehues</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Marcello</namePart>
            <namePart type="family">Frederico</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>International Conference on Spoken Language Translation</publisher>
            <place>
                <placeTerm type="text">Brussels</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper we present the ADAPT system built for the Basque to English Low Resource MT Evaluation Campaign. Basque is a low-resourced, morphologically-rich language. This poses a challenge for Neural Machine Translation models which usually achieve better performance when trained with large sets of data. Accordingly, we used synthetic data to improve the translation quality produced by a model built using only authentic data. Our proposal uses back-translated data to: (a) create new sentences, so the system can be trained with more data; and (b) translate sentences that are close to the test set, so the model can be fine-tuned to the document to be translated.</abstract>
    <identifier type="citekey">poncelas-etal-2018-adapt</identifier>
    <location>
        <url>https://aclanthology.org/2018.iwslt-1.11</url>
    </location>
    <part>
        <date>2018-oct 29-30</date>
        <extent unit="page">
            <start>76</start>
            <end>82</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T The ADAPT System Description for the IWSLT 2018 Basque to English Translation Task
%A Poncelas, Alberto
%A Way, Andy
%A Sarasola, Kepa
%Y Turchi, Marco
%Y Niehues, Jan
%Y Frederico, Marcello
%S Proceedings of the 15th International Conference on Spoken Language Translation
%D 2018
%8 oct 29 30
%I International Conference on Spoken Language Translation
%C Brussels
%F poncelas-etal-2018-adapt
%X In this paper we present the ADAPT system built for the Basque to English Low Resource MT Evaluation Campaign. Basque is a low-resourced, morphologically-rich language. This poses a challenge for Neural Machine Translation models which usually achieve better performance when trained with large sets of data. Accordingly, we used synthetic data to improve the translation quality produced by a model built using only authentic data. Our proposal uses back-translated data to: (a) create new sentences, so the system can be trained with more data; and (b) translate sentences that are close to the test set, so the model can be fine-tuned to the document to be translated.
%U https://aclanthology.org/2018.iwslt-1.11
%P 76-82

Markdown (Informal)

[The ADAPT System Description for the IWSLT 2018 Basque to English Translation Task](https://aclanthology.org/2018.iwslt-1.11) (Poncelas et al., IWSLT 2018)

• The ADAPT System Description for the IWSLT 2018 Basque to English Translation Task (Poncelas et al., IWSLT 2018)

ACL

• Alberto Poncelas, Andy Way, and Kepa Sarasola. 2018. The ADAPT System Description for the IWSLT 2018 Basque to English Translation Task. In Proceedings of the 15th International Conference on Spoken Language Translation, pages 76–82, Brussels. International Conference on Spoken Language Translation.