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Transliteration is a task in the domain of NLP where the output word is asimilar-sounding word written using the letters of any foreign language. Todaythis system has been developed for several language pairs that involve Englishas either the source or target word and deployed in several places like GoogleTranslate and chatbots. However, there is very little research done in thefield of Indic languages transliterated to other Indic languages. This paperdemonstrates a multilingual model based on transformers (with somemodifications) that can give noticeably higher performance and accuracy thanall existing models in this domain and get much better results thanstate-of-the-art models. This paper shows a model that can performtransliteration between any pair among the following five languages - English,Hindi, Bengali, Kannada and Tamil. It is applicable in scenarios where languageis a barrier to communication in any written task. The model beats thestate-of-the-art (for all pairs among the five mentioned languages - English,Hindi, Bengali, Kannada, and Tamil) and achieves a top-1 accuracy score of80.7%, about 29.5% higher than the best current results. Furthermore, the modelachieves 93.5% in terms of Phonetic Accuracy (transliteration is primarily aphonetic/sound-based task).

MATra: A Multilingual Attentive Transliteration System for Indian Scripts