The use of multi-lingualism in the new generation is widespread in the form of code-mixed data on social media, and therefore a robust translation system is required for catering to the monolingual users, as well as for easier comprehension by language processing models. In this work, we present a translation framework that uses translation-transliteration strategy for translating code-mixed data into their equivalent monolingual instances. For converting the output to a more fluent form, it is reordered using a target language model. The most important advantage of the proposed framework is that it does not requirea code-mixed to monolingual parallel corpus at any point. On testing the framework, it achieved BLEU and TER scores of 16.47 and 55.45, respectively. Since the proposed framework deals with various sub-modules, we dive deeper into the importance of each of them, analyze the errors and finally, discuss some improvement strategies.

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@article{mahata2019code, title={Code-Mixed to Monolingual Translation Framework}, author={Mahata, Sainik Kumar and Mandal, Soumil and Das, Dipankar and Bandyopadhyay, Sivaji}, journal={arXiv preprint arXiv:1911.03772}, year={2019} }