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Neural Machine Translation for Indian Languages

Authors
  • Pathak, Amarnath
  • Pakray, Partha
Type
Published Article
Journal
Journal of Intelligent Systems
Publisher
De Gruyter
Publication Date
Jun 18, 2018
Volume
28
Issue
3
Pages
465–477
Identifiers
DOI: 10.1515/jisys-2018-0065
Source
De Gruyter
Keywords
License
Yellow

Abstract

Machine Translation bridges communication barriers and eases interaction among people having different linguistic backgrounds. Machine Translation mechanisms exploit a range of techniques and linguistic resources for translation prediction. Neural machine translation (NMT), in particular, seeks optimality in translation through training of neural network, using a parallel corpus having a considerable number of instances in the form of a parallel running source and target sentences. Easy availability of parallel corpora for major Indian language forms and the ability of NMT systems to better analyze context and produce fluent translation make NMT a prominent choice for the translation of Indian languages. We have trained, tested, and analyzed NMT systems for English to Tamil, English to Hindi, and English to Punjabi translations. Predicted translations have been evaluated using Bilingual Evaluation Understudy and by human evaluators to assess the quality of translation in terms of its adequacy, fluency, and correspondence with human-predicted translation.

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