RNN-LSTM-GRU based language transformation

In past, rule-based and statistical machine translation techniques were employed to solve Urdu transliteration techniques. As mentioned in the literature, Urdu is considered as low-resource language. An impressive effort has been made for Arabic, French and Chinese language transliteration as compared to the Urdu language. Machine translation of Urdu language is a challenging problem. A very minute research work has been conducted toward Urdu transliteration. Factors behind the ignorance of Urdu language in research may be for its morphological complexity, diversity and most importantly due to the lack of reasonable bilingual parallel dataset. Getting a corpus for a language transliteration is the main resource to work on. This paper demonstrates the application of neural machine translation (NMT) for Urdu language transliteration, with the emphasis on contextual coverage of a language, which helps to improve transliteration accuracy. Build a robust NMT model which delivers efficient performance when trained over bilingual parallel corpora. Neural machine translation is an emerging technique depicting impressive performance, better than traditional MT methods in multiple aspects. In this research, we build the NMT model for the Urdu language to improve transliteration quality. An attention-based encoder–decoder system is proposed, and our experiment proves the efficiency of the proposed approach. To the best of our knowledge, this is the first effort for Urdu language bidirectional transliteration toward neural machine translation.