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Categorizing Emails Using Machine Learning with Textual Features Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Machine Translation on a Parallel Code-Switched Corpus

Authors : M. A. Menacer, D. Langlois, D. Jouvet, D. Fohr, O. Mella, K. Smaïli

Published in: Advances in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter An Experiment for Background Subtraction in a Dynamic Scene
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Footnotes
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Metadata
Title
Machine Translation on a Parallel Code-Switched Corpus
Authors
M. A. Menacer
D. Langlois
D. Jouvet
D. Fohr
O. Mella
K. Smaïli
Copyright Year
2019
Book
Advances in Artificial Intelligence

Print ISBN: 978-3-030-18304-2

Electronic ISBN: 978-3-030-18305-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-18305-9_40

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