@inproceedings{xu-etal-2022-boosting,
title = "Boosting Neural Machine Translation with Similar Translations",
author = "Xu, Jitao and
Crego, Josep and
Senellart, Jean",
editor = "Campbell, Janice and
Larocca, Stephen and
Marciano, Jay and
Savenkov, Konstantin and
Yanishevsky, Alex",
booktitle = "Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)",
month = sep,
year = "2022",
address = "Orlando, USA",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2022.amta-upg.20/",
pages = "282--292",
abstract = "This presentation demonstrates data augmentation methods for Neural Machine Translation to make use of similar translations, in a comparable way a human translator employs fuzzy matches. We show how we simply feed the neural model with information on both source and target sides of the fuzzy matches, and we also extend the similarity to include semantically related translations retrieved using distributed sentence representations. We show that translations based on fuzzy matching provide the model with {\textquotedblleft}copy{\textquotedblright} information while translations based on embedding similarities tend to extend the translation {\textquotedblleft}context{\textquotedblright}. Results indicate that the effect from both similar sentences are adding up to further boost accuracy, are combining naturally with model fine-tuning and are providing dynamic adaptation for unseen translation pairs. Tests on multiple data sets and domains show consistent accuracy improvements."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xu-etal-2022-boosting">
<titleInfo>
<title>Boosting Neural Machine Translation with Similar Translations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jitao</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Josep</namePart>
<namePart type="family">Crego</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jean</namePart>
<namePart type="family">Senellart</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Janice</namePart>
<namePart type="family">Campbell</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Stephen</namePart>
<namePart type="family">Larocca</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jay</namePart>
<namePart type="family">Marciano</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Konstantin</namePart>
<namePart type="family">Savenkov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alex</namePart>
<namePart type="family">Yanishevsky</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Machine Translation in the Americas</publisher>
<place>
<placeTerm type="text">Orlando, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This presentation demonstrates data augmentation methods for Neural Machine Translation to make use of similar translations, in a comparable way a human translator employs fuzzy matches. We show how we simply feed the neural model with information on both source and target sides of the fuzzy matches, and we also extend the similarity to include semantically related translations retrieved using distributed sentence representations. We show that translations based on fuzzy matching provide the model with “copy” information while translations based on embedding similarities tend to extend the translation “context”. Results indicate that the effect from both similar sentences are adding up to further boost accuracy, are combining naturally with model fine-tuning and are providing dynamic adaptation for unseen translation pairs. Tests on multiple data sets and domains show consistent accuracy improvements.</abstract>
<identifier type="citekey">xu-etal-2022-boosting</identifier>
<location>
<url>https://aclanthology.org/2022.amta-upg.20/</url>
</location>
<part>
<date>2022-09</date>
<extent unit="page">
<start>282</start>
<end>292</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Boosting Neural Machine Translation with Similar Translations
%A Xu, Jitao
%A Crego, Josep
%A Senellart, Jean
%Y Campbell, Janice
%Y Larocca, Stephen
%Y Marciano, Jay
%Y Savenkov, Konstantin
%Y Yanishevsky, Alex
%S Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)
%D 2022
%8 September
%I Association for Machine Translation in the Americas
%C Orlando, USA
%F xu-etal-2022-boosting
%X This presentation demonstrates data augmentation methods for Neural Machine Translation to make use of similar translations, in a comparable way a human translator employs fuzzy matches. We show how we simply feed the neural model with information on both source and target sides of the fuzzy matches, and we also extend the similarity to include semantically related translations retrieved using distributed sentence representations. We show that translations based on fuzzy matching provide the model with “copy” information while translations based on embedding similarities tend to extend the translation “context”. Results indicate that the effect from both similar sentences are adding up to further boost accuracy, are combining naturally with model fine-tuning and are providing dynamic adaptation for unseen translation pairs. Tests on multiple data sets and domains show consistent accuracy improvements.
%U https://aclanthology.org/2022.amta-upg.20/
%P 282-292
Markdown (Informal)
[Boosting Neural Machine Translation with Similar Translations](https://aclanthology.org/2022.amta-upg.20/) (Xu et al., AMTA 2022)
ACL
- Jitao Xu, Josep Crego, and Jean Senellart. 2022. Boosting Neural Machine Translation with Similar Translations. In Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track), pages 282–292, Orlando, USA. Association for Machine Translation in the Americas.