Code-Mixed to Monolingual Translation Framework
This addresses the need for robust translation systems to aid monolingual users and language processing models in handling widespread multilingual code-mixed data, though it appears incremental as it builds on translation-transliteration strategies.
The paper tackles the problem of translating code-mixed social media data into monolingual text, achieving BLEU and TER scores of 16.47 and 55.45 without requiring a parallel corpus.
The use of multilingualism 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 a 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 require a 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.