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Back-translation Approach for Code-switching Machine Translation: A Case Study


Maraim Masoud, Daniel Torregrosa, Paul Buitelaar, Mihael ArĨan

Publication Type: 
Refereed Conference Meeting Proceeding
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.
Conference Name: 
27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science
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Research Group: 
National University of Ireland, Galway (NUIG)
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