  Neural Machine Translation (NMT) is an ongoing technique for Machine
Translation (MT) using enormous artificial neural network. It has exhibited
promising outcomes and has shown incredible potential in solving challenging
machine translation exercises. One such exercise is the best approach to
furnish great MT to language sets with a little preparing information. In this
work, Zero Shot Translation (ZST) is inspected for a low resource language
pair. By working on high resource language pairs for which benchmarks are
available, namely Spanish to Portuguese, and training on data sets
(Spanish-English and English-Portuguese) we prepare a state of proof for ZST
system that gives appropriate results on the available data. Subsequently the
same architecture is tested for Sanskrit to Hindi translation for which data is
sparse, by training the model on English-Hindi and Sanskrit-English language
pairs. In order to prepare and decipher with ZST system, we broaden the
preparation and interpretation pipelines of NMT seq2seq model in tensorflow,
incorporating ZST features. Dimensionality reduction of word embedding is
performed to reduce the memory usage for data storage and to achieve a faster
training and translation cycles. In this work existing helpful technology has
been utilized in an imaginative manner to execute our NLP issue of Sanskrit to
Hindi translation. A Sanskrit-Hindi parallel corpus of 300 is constructed for
testing. The data required for the construction of parallel corpus has been
taken from the telecasted news, published on Department of Public Information,
state government of Madhya Pradesh, India website.
