Machine Translation on a Parallel Code-Switched Corpus

Code-switching (CS) is the phenomenon that occurs when a speaker alternates between two or more languages within an utterance or discourse. In this work, we investigate the existence of code-switching in formal text, namely proceedings of multilingual institutions. Our study is carried out on the Arabic-English code-mixing in a parallel corpus extracted from official documents of United Nations. We build a parallel code-switched corpus with two reference translations one in pure Arabic and the other in pure English. We also carry out a human evaluation of this resource in the aim to use it to evaluate the translation of code-switched documents. To the best of our knowledge, this kind of corpora does not exist. The one we propose is unique. This paper examines several methods to translate code-switched corpus: conventional statistical machine translation, the end-to-end neural machine translation and multitask-learning.