Muhammad Naufal Muzhaffar

A Systematic Review of Transfer Learning and Data Augmentation in Neural Machine Translation of Low-Resource Languages

The findings show that a hybrid combination of TL and DA with a self-supervised objective is the most effective solution for extremely low-resource scenarios, capable of producing the highest translation quality and outperforming baseline models and traditional methods such as Statistical Machine Translation (SMT).