@inproceedings{chen-etal-2024-improving-nmt,
title = "Improving {NMT} from a Low-Resource Source Language: A Use Case from {C}atalan to {C}hinese via {S}panish",
author = "Chen, Yongjian and
Toral, Antonio and
Li, Zhijian and
Farr{\'u}s, Mireia",
editor = "Scarton, Carolina and
Prescott, Charlotte and
Bayliss, Chris and
Oakley, Chris and
Wright, Joanna and
Wrigley, Stuart and
Song, Xingyi and
Gow-Smith, Edward and
Bawden, Rachel and
S{\'a}nchez-Cartagena, V{\'\i}ctor M and
Cadwell, Patrick and
Lapshinova-Koltunski, Ekaterina and
Cabarr{\~a}o, Vera and
Chatzitheodorou, Konstantinos and
Nurminen, Mary and
Kanojia, Diptesh and
Moniz, Helena",
booktitle = "Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)",
month = jun,
year = "2024",
address = "Sheffield, UK",
publisher = "European Association for Machine Translation (EAMT)",
url = "https://aclanthology.org/2024.eamt-1.20",
pages = "229--245",
abstract = "The effectiveness of neural machine translation is markedly constrained in low-resource scenarios, where the scarcity of parallel data hampers the development of robust models. This paper focuses on the scenario where the source language is low-resourceand there exists a related high-resource language, for which we introduce a novel approach that combines pivot translation and multilingual training. As a use case we tackle the automatic translation from Catalan to Chinese, using Spanish as an additional language. Our evaluation, conducted on the FLORES-200 benchmark, compares our new approach against a vanilla baseline alongside other models representing various low-resource techniques in the Catalan-to-Chinese context. Experimental results highlight the efficacy of our proposed method, which outperforms existing models, notably demonstrating significant improvements both in translation quality and in lexical diversity.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chen-etal-2024-improving-nmt">
<titleInfo>
<title>Improving NMT from a Low-Resource Source Language: A Use Case from Catalan to Chinese via Spanish</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yongjian</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Antonio</namePart>
<namePart type="family">Toral</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhijian</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mireia</namePart>
<namePart type="family">Farrús</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Carolina</namePart>
<namePart type="family">Scarton</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Charlotte</namePart>
<namePart type="family">Prescott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chris</namePart>
<namePart type="family">Bayliss</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chris</namePart>
<namePart type="family">Oakley</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joanna</namePart>
<namePart type="family">Wright</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Stuart</namePart>
<namePart type="family">Wrigley</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xingyi</namePart>
<namePart type="family">Song</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Edward</namePart>
<namePart type="family">Gow-Smith</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rachel</namePart>
<namePart type="family">Bawden</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Víctor</namePart>
<namePart type="given">M</namePart>
<namePart type="family">Sánchez-Cartagena</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Patrick</namePart>
<namePart type="family">Cadwell</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Lapshinova-Koltunski</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Vera</namePart>
<namePart type="family">Cabarrão</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Konstantinos</namePart>
<namePart type="family">Chatzitheodorou</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">Diptesh</namePart>
<namePart type="family">Kanojia</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>
<originInfo>
<publisher>European Association for Machine Translation (EAMT)</publisher>
<place>
<placeTerm type="text">Sheffield, UK</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The effectiveness of neural machine translation is markedly constrained in low-resource scenarios, where the scarcity of parallel data hampers the development of robust models. This paper focuses on the scenario where the source language is low-resourceand there exists a related high-resource language, for which we introduce a novel approach that combines pivot translation and multilingual training. As a use case we tackle the automatic translation from Catalan to Chinese, using Spanish as an additional language. Our evaluation, conducted on the FLORES-200 benchmark, compares our new approach against a vanilla baseline alongside other models representing various low-resource techniques in the Catalan-to-Chinese context. Experimental results highlight the efficacy of our proposed method, which outperforms existing models, notably demonstrating significant improvements both in translation quality and in lexical diversity.</abstract>
<identifier type="citekey">chen-etal-2024-improving-nmt</identifier>
<location>
<url>https://aclanthology.org/2024.eamt-1.20</url>
</location>
<part>
<date>2024-06</date>
<extent unit="page">
<start>229</start>
<end>245</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Improving NMT from a Low-Resource Source Language: A Use Case from Catalan to Chinese via Spanish
%A Chen, Yongjian
%A Toral, Antonio
%A Li, Zhijian
%A Farrús, Mireia
%Y Scarton, Carolina
%Y Prescott, Charlotte
%Y Bayliss, Chris
%Y Oakley, Chris
%Y Wright, Joanna
%Y Wrigley, Stuart
%Y Song, Xingyi
%Y Gow-Smith, Edward
%Y Bawden, Rachel
%Y Sánchez-Cartagena, Víctor M.
%Y Cadwell, Patrick
%Y Lapshinova-Koltunski, Ekaterina
%Y Cabarrão, Vera
%Y Chatzitheodorou, Konstantinos
%Y Nurminen, Mary
%Y Kanojia, Diptesh
%Y Moniz, Helena
%S Proceedings of the 25th Annual Conference of the European Association for Machine Translation (Volume 1)
%D 2024
%8 June
%I European Association for Machine Translation (EAMT)
%C Sheffield, UK
%F chen-etal-2024-improving-nmt
%X The effectiveness of neural machine translation is markedly constrained in low-resource scenarios, where the scarcity of parallel data hampers the development of robust models. This paper focuses on the scenario where the source language is low-resourceand there exists a related high-resource language, for which we introduce a novel approach that combines pivot translation and multilingual training. As a use case we tackle the automatic translation from Catalan to Chinese, using Spanish as an additional language. Our evaluation, conducted on the FLORES-200 benchmark, compares our new approach against a vanilla baseline alongside other models representing various low-resource techniques in the Catalan-to-Chinese context. Experimental results highlight the efficacy of our proposed method, which outperforms existing models, notably demonstrating significant improvements both in translation quality and in lexical diversity.
%U https://aclanthology.org/2024.eamt-1.20
%P 229-245
Markdown (Informal)
[Improving NMT from a Low-Resource Source Language: A Use Case from Catalan to Chinese via Spanish](https://aclanthology.org/2024.eamt-1.20) (Chen et al., EAMT 2024)
ACL