@inproceedings{stefaniak-2020-evaluating,
title = "Evaluating the usefulness of neural machine translation for the {P}olish translators in the {E}uropean Commission",
author = "Stefaniak, Karolina",
editor = "Martins, Andr{\'e} and
Moniz, Helena and
Fumega, Sara and
Martins, Bruno and
Batista, Fernando and
Coheur, Luisa and
Parra, Carla and
Trancoso, Isabel and
Turchi, Marco and
Bisazza, Arianna and
Moorkens, Joss and
Guerberof, Ana and
Nurminen, Mary and
Marg, Lena and
Forcada, Mikel L.",
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.28",
pages = "263--269",
abstract = "The mission of the Directorate General for Translation (DGT) is to provide high-quality translation to help the European Commission communicate with EU citizens. To this end DGT employs almost 2000 translators from all EU official languages. But while the demand for translation has been continuously growing, following a global trend, the number of translators has decreased. To cope with the demand, DGT extensively uses a CAT environment encompassing translation memories, terminology databases and recently also machine translation. This paper examines the benefits and risks of using neural machine translation to augment the productivity of in‒house DGT translators for the English‒Polish language pair. Based on the analysis of a sample of NMT‒translated texts and on the observations of the working practices of Polish translators it is concluded that the possible productivity gain is still modest, while the risks to quality are quite substantial.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="stefaniak-2020-evaluating">
<titleInfo>
<title>Evaluating the usefulness of neural machine translation for the Polish translators in the European Commission</title>
</titleInfo>
<name type="personal">
<namePart type="given">Karolina</namePart>
<namePart type="family">Stefaniak</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 22nd Annual Conference of the European Association for Machine Translation</title>
</titleInfo>
<name type="personal">
<namePart type="given">André</namePart>
<namePart type="family">Martins</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Helena</namePart>
<namePart type="family">Moniz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sara</namePart>
<namePart type="family">Fumega</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bruno</namePart>
<namePart type="family">Martins</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fernando</namePart>
<namePart type="family">Batista</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Luisa</namePart>
<namePart type="family">Coheur</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Carla</namePart>
<namePart type="family">Parra</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Isabel</namePart>
<namePart type="family">Trancoso</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<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">Arianna</namePart>
<namePart type="family">Bisazza</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joss</namePart>
<namePart type="family">Moorkens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ana</namePart>
<namePart type="family">Guerberof</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mary</namePart>
<namePart type="family">Nurminen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lena</namePart>
<namePart type="family">Marg</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mikel</namePart>
<namePart type="given">L</namePart>
<namePart type="family">Forcada</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>European Association for Machine Translation</publisher>
<place>
<placeTerm type="text">Lisboa, Portugal</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The mission of the Directorate General for Translation (DGT) is to provide high-quality translation to help the European Commission communicate with EU citizens. To this end DGT employs almost 2000 translators from all EU official languages. But while the demand for translation has been continuously growing, following a global trend, the number of translators has decreased. To cope with the demand, DGT extensively uses a CAT environment encompassing translation memories, terminology databases and recently also machine translation. This paper examines the benefits and risks of using neural machine translation to augment the productivity of in‒house DGT translators for the English‒Polish language pair. Based on the analysis of a sample of NMT‒translated texts and on the observations of the working practices of Polish translators it is concluded that the possible productivity gain is still modest, while the risks to quality are quite substantial.</abstract>
<identifier type="citekey">stefaniak-2020-evaluating</identifier>
<location>
<url>https://aclanthology.org/2020.eamt-1.28</url>
</location>
<part>
<date>2020-11</date>
<extent unit="page">
<start>263</start>
<end>269</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Evaluating the usefulness of neural machine translation for the Polish translators in the European Commission
%A Stefaniak, Karolina
%Y Martins, André
%Y Moniz, Helena
%Y Fumega, Sara
%Y Martins, Bruno
%Y Batista, Fernando
%Y Coheur, Luisa
%Y Parra, Carla
%Y Trancoso, Isabel
%Y Turchi, Marco
%Y Bisazza, Arianna
%Y Moorkens, Joss
%Y Guerberof, Ana
%Y Nurminen, Mary
%Y Marg, Lena
%Y Forcada, Mikel L.
%S Proceedings of the 22nd Annual Conference of the European Association for Machine Translation
%D 2020
%8 November
%I European Association for Machine Translation
%C Lisboa, Portugal
%F stefaniak-2020-evaluating
%X The mission of the Directorate General for Translation (DGT) is to provide high-quality translation to help the European Commission communicate with EU citizens. To this end DGT employs almost 2000 translators from all EU official languages. But while the demand for translation has been continuously growing, following a global trend, the number of translators has decreased. To cope with the demand, DGT extensively uses a CAT environment encompassing translation memories, terminology databases and recently also machine translation. This paper examines the benefits and risks of using neural machine translation to augment the productivity of in‒house DGT translators for the English‒Polish language pair. Based on the analysis of a sample of NMT‒translated texts and on the observations of the working practices of Polish translators it is concluded that the possible productivity gain is still modest, while the risks to quality are quite substantial.
%U https://aclanthology.org/2020.eamt-1.28
%P 263-269
Markdown (Informal)
[Evaluating the usefulness of neural machine translation for the Polish translators in the European Commission](https://aclanthology.org/2020.eamt-1.28) (Stefaniak, EAMT 2020)
ACL