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English–Mizo Machine Translation using neural and statistical approaches

  • Amarnath Pathak
  • Partha Pakray
  • Jereemi Bentham
Original Article
  • 154 Downloads

Abstract

Machine translation helps resolve language incomprehensibility issues and eases interaction among people from varying linguistic backgrounds. Although corpus-based approaches (statistical and neural) offer reasonable translation accuracy for large-sized corpus, robustness of such approaches lie in their ability to adapt to low-resource languages, which confront unavailability of large-sized corpus. In this paper, prediction aptness of two approaches has been meticulously explored in the context of Mizo, a low-resource Indian language. Translations predicted by the two approaches have been comparatively and adequately analyzed on a number of grounds to infer their strengths and weaknesses, particularly in low-resource scenarios.

Keywords

Neural Machine Translation (NMT) Statistical Machine Translation (SMT) BLEU score Attention mechanism 

Notes

Acknowledgements

Authors would like to thank Department of Computer Science and Engineering, National Institute of Technology Mizoram, for providing the requisite support and infrastructure to execute this work.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • Amarnath Pathak
    • 1
  • Partha Pakray
    • 1
  • Jereemi Bentham
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of Technology MizoramAizawlIndia

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