Improved English to Hindi MT using Long Short-Term Memory

Abstract

It is in the field of language translation that robots are unable to compete with humans. Statistical Machine Translation (SMT) is one of the traditional methods for solving the Machine Translation (MT) problem. This approach is best suited for grammatically organized language pairings with comparable syntax, and it necessitates a large number of training data sets. Recent years have seen the rise of Neural Machine Translation (NMT) as a potential alternate way of dealing with the same issue. Various NMT systems for the Indian language Hindi are examined in this study. Earlier NMT approaches have the problem with longer sequences and not being able to capture object importance. In this proposed solution, Long Short-Term Memory (LSTM) mechanism has been used which efficiently deals with such problems. When translating from English to Hindi, eight different architectures of NMT are experimented and the results are compared with those of more standard MT methods. NMT only needs a tiny parallel corpus for training, which means that it can handle tens of thousands of training data sets with ease.