Joint layer based deep learning framework for bilingual machine transliteration

Between the growth of Internet or World Wide Web (WWW) and the emersion of the social networking site like Friendster, Myspace etc., information society started facing exhilarating challenges in language technology applications such as Machine Translation (MT) and Information Retrieval (IR). Nevertheless, there were researchers working in Machine Translation that deal with real time information for over 50 years since the first computer has come along. Merely, the need for translating data has become larger than before as the world was getting together through social media. Especially, translating proper nouns and technical terms has become openly challenging task in Machine Translation. The Machine transliteration was emerged as a part of information retrieval and machine translation projects to translate the Named Entities based on phoneme and grapheme, hence, those are not registered in the dictionary. Many researchers have used approaches such as conventional Graphical models and also adopted other machine translation techniques for Machine Transliteration. Machine Transliteration was always looked as a Machine Learning Problem. In this paper, we presented a new area of Machine Learning approach termed as a Deep Learning for improving the bilingual machine transliteration task for Tamil and English languages with limited corpus. This technique precedes Artificial Intelligence. The system is built on Deep Belief Network (DBN), a generative graphical model, which has been proved to work well with other Machine Learning problem. We have obtained 79.46% accuracy for English to Tamil transliteration task and 78.4 % for Tamil to English transliteration.