article

Improving statistical machine translation for a resource-poor language using related resource-rich languages

Authors:

Hwee Tou NgAuthors Info & Claims

Journal of Artificial Intelligence Research, Volume 44, Issue 1

Pages 179 - 222

Published: 01 May 2012 Publication History

Abstract

We propose a novel language-independent approach for improving machine translation for resource-poor languages by exploiting their similarity to resource-rich ones. More precisely, we improve the translation from a resource-poor source language X₁ into a resourcerich language Y given a bi-text containing a limited number of parallel sentences for X₁-Y and a larger bi-text for X₂-Y for some resource-rich language X₂ that is closely related to X₁. This is achieved by taking advantage of the opportunities that vocabulary overlap and similarities between the languages X₁ and X₂ in spelling, word order, and syntax offer: (1) we improve the word alignments for the resource-poor language, (2) we further augment it with additional translation options, and (3) we take care of potential spelling differences through appropriate transliteration. The evaluation for Indonesian → English using Malay and for Spanish → English using Portuguese and pretending Spanish is resource-poor shows an absolute gain of up to 1.35 and 3.37 BLEU points, respectively, which is an improvement over the best rivaling approaches, while using much less additional data. Overall, our method cuts the amount of necessary "real" training data by a factor of 2-5.

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Journal of Artificial Intelligence Research Volume 44, Issue 1

May 2012

717 pages

ISSN:1076-9757

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Published: 01 May 2012

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Karakanta ADehdari JGenabith J(2018)Neural machine translation for low-resource languages without parallel corporaMachine Translation10.1007/s10590-017-9203-532:1-2(167-189)Online publication date: 1-Jun-2018
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