Unsupervised Arabic dialect segmentation for machine translation. (23rd March 2022)

• Record Type: Journal Article
• Authors: Salloum, Wael; Habash, Nizar
• Is Part Of: Natural language engineering. Volume 28:Number 2(2022)
• Page Start: 223
• Page End: 248
• Publication Date: 2022-03-23
• Subjects: Machine translation -- Morphology -- Arabic dialects -- Unsupervised learning; Natural language processing (Computer science) -- Periodicals; Software engineering -- Periodicals; 006.35
• DOI: 10.1017/S1351324920000455
• Languages: English
• ISSNs: 1351-3249

Abstract

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.