Deep Neural-Based Machine Translation Using RNN for Indian Languages

This study describes how to develop a machine learning system for the translation of Indian languages (Hindi, Gujarati, and Punjabi) using a form of deep neural network (DNN). We are employing a form of RNN called long short-term memory (LSTM). The state-of-the-art algorithm for sequential data is recurrent neural networks (RNNs). This research work pertaining to RNN-based translation of Indian Languages involved experimenting with nmt models (unidirectional nmt and bidirectional) and their hyperparams to devise results important from real-world perspective. We employed the neural machine translation (NMT) model to get the best results. Unlike the standard phrase-based translation system, which consists of many small sub-components that are tweaked independently, this approach allows a single system to be trained directly on the source and target text and attempts at neural machine translation.