Transformers for Low-resource Neural Machine Translation

Abstract

The recent advances in neural machine translation enable it to be state-of-the-art. However, although there are significant improvements in neural machine translation for a few high-resource languages, its performance is still low for less-resourced languages as the amount of training data significantly affects the quality of the machine translation models. Therefore, identifying a neural machine translation architecture that can train the best models in low-data conditions is essential for less-resourced languages. This research modified the Transformer-based neural machine translation architectures for low-resource polysynthetic languages. Our proposed system outperformed the strong baseline in the automatic evaluation of the experiments on the public benchmark datasets.