A Morphologically-Aware Dictionary-based Data Augmentation Technique for Machine Translation of Under-Represented Languages

The availability of parallel texts is crucial to the performance of machine translation models. However, most of the world's languages face the predominant challenge of data scarcity. In this paper, we propose strategies to synthesize parallel data relying on morpho-syntactic information and using bilingual lexicons along with a small amount of seed parallel data. Our methodology adheres to a realistic scenario backed by the small parallel seed data. It is linguistically informed, as it aims to create augmented data that …

augmentation availability bilingual challenge cs.cl data dictionary face information languages machine machine translation paper performance strategies translation world