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Unsupervised Arabic dialect segmentation for machine translation

Published online by Cambridge University Press:  23 September 2020

Wael Salloum  and
Nizar Habash
Wael Salloum*
Affiliation:
AI Research Department, Mendel.ai, San Jose, CA, USA
Nizar Habash
Affiliation:
AI Research Department, Mendel.ai, San Jose, CA, USA
*
*Corresponding author. E-mail: wael@ccls.columbia.edu
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Abstract

Resource-limited and morphologically rich languages pose many challenges to natural language processing tasks. Their highly inflected surface forms inflate the vocabulary size and increase sparsity in an already scarce data situation. In this article, we present an unsupervised learning approach to vocabulary reduction through morphological segmentation. We demonstrate its value in the context of machine translation for dialectal Arabic (DA), the primarily spoken, orthographically unstandardized, morphologically rich and yet resource poor variants of Standard Arabic. Our approach exploits the existence of monolingual and parallel data. We show comparable performance to state-of-the-art supervised methods for DA segmentation.

Keywords

Machine translationMorphologyArabic dialectsUnsupervised learning
Type
Article
Information
Natural Language Engineering , Volume 28 , Issue 2 , March 2022 , pp. 223 - 248
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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