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

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.