Translation systems rely on a large and goodquality parallel corpus for producing reliable translations. However, obtaining such a corpus for low-resourced languages is a challenge. New research has shown that transfer learning can mitigate this issue by augmenting lowresourced MT systems with high-resourced ones. In this work, we explore two types of transfer learning techniques, namely, crosslingual transfer learning and multilingual training, both with information augmentation, to examine the degree of performance improvement following the augmentation. Furthermore, we use languages of the same family (Romanic, in our case), to investigate the role of the shared linguistic property, in producing dependable translations.

In this work, we focus on the development of machine translation (MT) models of a lowresource language pair viz. English-Manipuri. Manipuri is one of the eight scheduled languages of the Indian constitution. Manipuri is currently written in two different scripts: one is its original script called Meitei Mayek and the other is the Bengali script. We evaluate the performance of English-Manipuri MT models based on transformer and transfer learning technique. Our MT models are trained using a dataset of 69,065 parallel sentences and validated on 500 sentences. Using 500 test sentences, the English to Manipuri MT models achieved a BLEU score of 19.13 and 29.05 with mT5 and OpenNMT respectively. The results demonstrate that the OpenNMT model significantly outperforms the mT5 model. Additionally, Manipuri to English MT system trained with OpenNMT model reported a BLEU score of 30.90. We also carried out a comparative analysis between the Bengali script and the transliterated Meitei Mayek script for English-Manipuri MT models. This analysis reveals that the transliterated version enhances the MT model performance resulting in a notable +2.35 improvement in the BLEU score.