  There have been very few attempts to benchmark performances of
state-of-the-art algorithms for Neural Machine Translation task on Indian
Languages. Google, Bing, Facebook and Yandex are some of the very few companies
which have built translation systems for few of the Indian Languages. Among
them, translation results from Google are supposed to be better, based on
general inspection. Bing-Translator do not even support Marathi language which
has around 95 million speakers and ranks 15th in the world in terms of combined
primary and secondary speakers. In this exercise, we trained and compared
variety of Neural Machine Marathi to English Translators trained with
BERT-tokenizer by huggingface and various Transformer based architectures using
Facebook's Fairseq platform with limited but almost correct parallel corpus to
achieve better BLEU scores than Google on Tatoeba and Wikimedia open datasets.
