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Neural Machine Translation for English to Hindi

Language translation is one task in which machine is definitely lagging behind the cognitive powers of human beings. Statistical Machine Translation is one of the conventional ways of solving the problem of machine translation. This method requires huge data sets and performs well on similar grammar structured language pairs. In recent years, Neural Machine Translation (NMT) has emerged as an alternate way of addressing the same issue. In this paper, we explore different configurations for setting up a Neural Machine Translation System for Indian language Hindi. We have experimented with eight different architecture combinations of NMT for English to Hindi and compared our results with conventional machine translation techniques. We have also observed in this work that NMT requires very less amount of data size for training and thus exhibits satisfactory translation for few thousands of training sentences as well.