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

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Machine Translation

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.

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Notes

  1. Tuning with a held-out subset of training data results in lower BLEU scores in all the experiments but does not change the conclusions of this paper.

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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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    Nima Pourdamghani & Kevin Knight

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  1. Nima Pourdamghani
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Correspondence to Nima Pourdamghani.

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This work is a significant extension to Pourdamghani and Knight (2017). The cipher model (Sects. 4.1, 5.1, and 6 ) and the evaluation of the RL to IL translation accuracy (Sect. 8.1) are initially presented in Pourdamghani and Knight (2017). Other sections including description of the language models and their training as well as the idea of converting the parallel data, methods for combining converted and original parallel data and machine translation experiments are presented in this paper for the first time.

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Pourdamghani, N., Knight, K. Neighbors helping the poor: improving low-resource machine translation using related languages. Machine Translation 33, 239–258 (2019). https://doi.org/10.1007/s10590-019-09236-7

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