English-Basque statistical and neural machine translation

Unanue, IJ; Arratibel, LG; Borzeshi, EZ; Piccardi, M

English-Basque statistical and neural machine translation

Unanue, IJ Arratibel, LG Borzeshi, EZ Piccardi, M

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Publication Type:: Conference Proceeding
Citation:: LREC 2018 - 11th International Conference on Language Resources and Evaluation, 2019, pp. 880 - 885
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Unanue, IJ	en_US
dc.contributor.author	Arratibel, LG	en_US
dc.contributor.author	Borzeshi, EZ	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.available	2017-12-13	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	LREC 2018 - 11th International Conference on Language Resources and Evaluation, 2019, pp. 880 - 885	en_US
dc.identifier.isbn	9791095546009	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128991
dc.description.abstract	© LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved. Neural Machine Translation (NMT) has attracted increasing attention in the recent years. However, it tends to require very large training corpora which could prove problematic for languages with low resources. For this reason, Statistical Machine Translation (SMT) continues to be a popular approach for low-resource language pairs. In this work, we address English-Basque translation and compare the performance of three contemporary statistical and neural machine translation systems: OpenNMT, Moses SMT and Google Translate. For evaluation, we employ an open-domain and an IT-domain corpora from the WMT16 resources for machine translation. In addition, we release a small dataset (Berriak) of 500 highly-accurate English-Basque translations of complex sentences useful for a thorough testing of the translation systems.	en_US
dc.relation.ispartof	LREC 2018 - 11th International Conference on Language Resources and Evaluation	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	English-Basque statistical and neural machine translation	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© LREC 2018 - 11th International Conference on Language Resources and Evaluation. All rights reserved. Neural Machine Translation (NMT) has attracted increasing attention in the recent years. However, it tends to require very large training corpora which could prove problematic for languages with low resources. For this reason, Statistical Machine Translation (SMT) continues to be a popular approach for low-resource language pairs. In this work, we address English-Basque translation and compare the performance of three contemporary statistical and neural machine translation systems: OpenNMT, Moses SMT and Google Translate. For evaluation, we employ an open-domain and an IT-domain corpora from the WMT16 resources for machine translation. In addition, we release a small dataset (Berriak) of 500 highly-accurate English-Basque translations of complex sentences useful for a thorough testing of the translation systems.

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http://hdl.handle.net/10453/128991