Multilingual Neural Machine Translation for Low-Resource Languages

In recent years, Neural Machine Translation (NMT) has been shown to be more effective than phrase-based statistical methods, thus quickly becoming the state of the art in machine translation (MT). However, NMT systems are limited in translating low-resourced languages, due to the significant amount...

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Autores principales:	Surafel M. Lakew, Marcello Federico, Matteo Negri, Marco Turchi
Formato:	article
Lenguaje:	EN
Publicado:	Accademia University Press 2018
Materias:	Social Sciences H Computational linguistics. Natural language processing P98-98.5
Acceso en línea:	https://doaj.org/article/2b007dd0adc846848c6d4c6ca15810d3
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spelling	oai:doaj.org-article:2b007dd0adc846848c6d4c6ca15810d32021-12-02T09:52:26ZMultilingual Neural Machine Translation for Low-Resource Languages2499-455310.4000/ijcol.531https://doaj.org/article/2b007dd0adc846848c6d4c6ca15810d32018-06-01T00:00:00Zhttp://journals.openedition.org/ijcol/531https://doaj.org/toc/2499-4553In recent years, Neural Machine Translation (NMT) has been shown to be more effective than phrase-based statistical methods, thus quickly becoming the state of the art in machine translation (MT). However, NMT systems are limited in translating low-resourced languages, due to the significant amount of parallel data that is required to learn useful mappings between languages. In this work, we show how the so-called multilingual NMT can help to tackle the challenges associated with low-resourced language translation. The underlying principle of multilingual NMT is to force the creation of hidden representations of words in a shared semantic space across multiple languages, thus enabling a positive parameter transfer across languages. Along this direction, we present multilingual translation experiments with three languages (English, Italian, Romanian) covering six translation directions, utilizing both recurrent neural networks and transformer (or self-attentive) neural networks. We then focus on the zero-shot translation problem, that is how to leverage multi-lingual data in order to learn translation directions that are not covered by the available training material. To this aim, we introduce our recently proposed iterative self-training method, which incrementally improves a multilingual NMT on a zero-shot direction by just relying on monolingual data. Our results on TED talks data show that multilingual NMT outperforms conventional bilingual NMT, that the transformer NMT outperforms recurrent NMT, and that zero-shot NMT outperforms conventional pivoting methods and even matches the performance of a fully-trained bilingual system.Surafel M. LakewMarcello FedericoMatteo NegriMarco TurchiAccademia University PressarticleSocial SciencesHComputational linguistics. Natural language processingP98-98.5ENIJCoL, Vol 4, Iss 1, Pp 11-25 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Social Sciences H Computational linguistics. Natural language processing P98-98.5
spellingShingle	Social Sciences H Computational linguistics. Natural language processing P98-98.5 Surafel M. Lakew Marcello Federico Matteo Negri Marco Turchi Multilingual Neural Machine Translation for Low-Resource Languages
description	In recent years, Neural Machine Translation (NMT) has been shown to be more effective than phrase-based statistical methods, thus quickly becoming the state of the art in machine translation (MT). However, NMT systems are limited in translating low-resourced languages, due to the significant amount of parallel data that is required to learn useful mappings between languages. In this work, we show how the so-called multilingual NMT can help to tackle the challenges associated with low-resourced language translation. The underlying principle of multilingual NMT is to force the creation of hidden representations of words in a shared semantic space across multiple languages, thus enabling a positive parameter transfer across languages. Along this direction, we present multilingual translation experiments with three languages (English, Italian, Romanian) covering six translation directions, utilizing both recurrent neural networks and transformer (or self-attentive) neural networks. We then focus on the zero-shot translation problem, that is how to leverage multi-lingual data in order to learn translation directions that are not covered by the available training material. To this aim, we introduce our recently proposed iterative self-training method, which incrementally improves a multilingual NMT on a zero-shot direction by just relying on monolingual data. Our results on TED talks data show that multilingual NMT outperforms conventional bilingual NMT, that the transformer NMT outperforms recurrent NMT, and that zero-shot NMT outperforms conventional pivoting methods and even matches the performance of a fully-trained bilingual system.
format	article
author	Surafel M. Lakew Marcello Federico Matteo Negri Marco Turchi
author_facet	Surafel M. Lakew Marcello Federico Matteo Negri Marco Turchi
author_sort	Surafel M. Lakew
title	Multilingual Neural Machine Translation for Low-Resource Languages
title_short	Multilingual Neural Machine Translation for Low-Resource Languages
title_full	Multilingual Neural Machine Translation for Low-Resource Languages
title_fullStr	Multilingual Neural Machine Translation for Low-Resource Languages
title_full_unstemmed	Multilingual Neural Machine Translation for Low-Resource Languages
title_sort	multilingual neural machine translation for low-resource languages
publisher	Accademia University Press
publishDate	2018
url	https://doaj.org/article/2b007dd0adc846848c6d4c6ca15810d3
work_keys_str_mv	AT surafelmlakew multilingualneuralmachinetranslationforlowresourcelanguages AT marcellofederico multilingualneuralmachinetranslationforlowresourcelanguages AT matteonegri multilingualneuralmachinetranslationforlowresourcelanguages AT marcoturchi multilingualneuralmachinetranslationforlowresourcelanguages
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Multilingual Neural Machine Translation for Low-Resource Languages

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