Code-Mixed to Monolingual Translation Framework

TL;DR

This paper introduces a novel translation framework that converts code-mixed social media data into monolingual text using translation and transliteration without requiring parallel corpora, enhancing language processing capabilities.

Contribution

The proposed framework uniquely combines translation, transliteration, and reordering without needing parallel corpora, improving code-mixed data translation for monolingual users.

Findings

Achieved BLEU score of 16.47

Achieved TER score of 55.45

Analyzed sub-module importance and error types

Abstract

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.