Back-translation approach for code-switching machine translation: A case study

Recently, machine translation has demonstrated significant progress in terms of translation quality. However, most of the research has focused on translating with pure monolingual texts in the source and the target side of the parallel corpora, when in fact code-switching is very common in communication nowadays. Despite the importance of handling code-switching in the translation task, existing machine translation systems fail to accommodate the code-switching content. In this paper, we examine the phenomenon of code-switching in machine translation for low-resource languages. Through different approaches, we evaluate the performance of our systems and make some observations about the role of code-mixing in the available corpora.This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under grant agreement number SFI/12/RC/2289_P2, co-funded by the European Regional Development Fund, and the Enterprise Ireland (EI) Innovation Partnership Programme under grant number IP20180729, NURS – Neural Machine Translation for Under-Resourced Scenarios.non-peer-reviewe