Neural Machine Translation (NMT) is known to be extremely challenging for Low-Resource Languages (LRL) with complex morphology. This work deals with the NMT of a specific LRL called Manipuri/Meeteilon, which is a highly agglutinative language where words have extensive suffixation with limited prefixation. The work studies and discusses the impacts of approaches to mitigate the issues of NMT involving agglutinative LRL in a strictly low-resource setting. The research work experimented with several methods and techniques including subword tokenization, tuning of the selfattention-based NMT model, utilization of monolingual corpus by iterative backtranslation, embedding-based sentence filtering for back translation. This research work in the strictly low resource setting of only 21204 training sentences showed remarkable results with a BLEU score of 28.17 for Manipuri to English translation.

Due to a lack of parallel data, low-resource language machine translation has been unable to make the most of Neural Machine Translation. This paper investigates several approaches as to how low-resource Neural Machine Translation can be improved in a strictly low-resource setting, especially for bidirectional Khasi-English language pairs. The back-translation method is used to expand the parallel corpus using monolingual data. The work also experimented with subword tokenizers to improve the translation accuracy for new and rare words. Transformer, a cutting-edge NMT model, serves as the backbone of the bidirectional Khasi-English machine translation. The final Khasi-to-English and English-to-Khasi NMT models trained using both authentic and synthetic parallel corpora show an increase of 2.34 and 3.1 BLEU scores, respectively, when compared to the models trained using only authentic parallel dataset.