Addressing data scarcity issue for English–Mizo neural machine translation using data augmentation and language model

Abstract

Low-resource language in machine translation systems poses multiple complications regarding accuracy in translation due to insufficient incorporation of linguistic information. The difference in the linguistic information between the language pair also significantly impacts the dataset creation for improving translation accuracy. Although neural machine translation achieves a state-of-the-art approach, dealing with low-resource language is challenging since it struggled with limited resources. This paper attempts to address the data scarcity problem using augmentation of synthetic parallel sentences, source-target phrase pairs, and language models at the target side for English-to-Mizo and Mizo-to-English translation via transformer-based neural machine translation. We have attained state-of-the-art results for both directions of translation.