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Machine Translation

, Volume 33, Issue 3, pp 239–258 | Cite as

Neighbors helping the poor: improving low-resource machine translation using related languages

  • Nima PourdamghaniEmail author
  • Kevin Knight
Article

Abstract

Sentence-level parallel data is essential for training machine translation systems. However, existing parallel data is extremely limited for thousands of languages. In order to increase the available parallel data for a low-resource language we borrow parallel data from a higher-resource closely related language (RL). In so doing we propose a method for translating texts from RL to the low-resource language without requiring any parallel data between them. We use this method to convert RL/English parallel data and use it as an extra resource for machine translation. We show that this extra parallel data highly helps the BLEU score.

Keywords

Low-resource Machine translation Deciphering Related languages 

Notes

Acknowledgements

This work was supported by DARPA Contract HR0011-15-C-0115. The authors would like to thank Marjan Ghazvininejad, Ulf Hermjakob, Jonathan May, and Michael Pust for their comments and suggestions.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Marina Del ReyUSA

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