A Morphologically-Aware Dictionary-based Data Augmentation Technique for Machine Translation of Under-Represented Languages
This addresses the challenge of data scarcity for machine translation of under-represented languages, though it is incremental as it builds on existing augmentation methods with linguistic insights.
The paper tackles the problem of data scarcity for machine translation of under-represented languages by proposing a morphologically-aware dictionary-based data augmentation technique, resulting in consistent performance improvements across 14 languages, including very low-resource ones, even with only five seed sentences and a bilingual lexicon.
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 is more likely to be grammatically correct. We analyze how our synthetic data can be combined with raw parallel data and demonstrate a consistent improvement in performance in our experiments on 14 languages (28 English <-> X pairs) ranging from well- to very low-resource ones. Our method leads to improvements even when using only five seed sentences and a bilingual lexicon.