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.
CITATION STYLE
Pourdamghani, N., & Knight, K. (2019). Neighbors helping the poor: improving low-resource machine translation using related languages. Machine Translation, 33(3), 239–258. https://doi.org/10.1007/s10590-019-09236-7
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