Development of Neural Machine Translation Systems for Indian Languages

Abstract

Machine Translation has became a very promising field with the recent advent of Neural Machine Translation (NMT) systems.A lot of work has been done in a small span of time, starting from bilingual models to complex multilingual models which can incorporate many languages at once. Building NMT systems for Indian languages is not an easy task and we often have to embed linguistic information to make it more robust and effective.The literature survey enlists different neural machine translation approaches as well as some research done on direct speech to speech translation. However, in the recent years the standard practices in NMT which are supervised in nature have not been upto mark so semi-supervised and unsupervised efforts have also been in existence. In this report we have compiled all the efforts and experiments done so far to develop neural machine translation systems for Indian languages and related components , starting from baseline modelling to use of feature injections , applying transfer learning, use of language models