Improving neural machine translation for low resource languages through non-parallel corpora: a case study of Egyptian dialect to modern standard Arabic translation

Improving neural machine translation for low resource languages through non-parallel corpora: a case study of Egyptian dialect to modern standard Arabic translation

Machine translation for low-resource languages poses significant challenges, primarily due to the limited availability of data. In recent years, unsupervised learning has emerged as a promising approach to overcome this issue by aiming to learn translations between languages without depending on par...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 2265 - 10
Main Authors: Faheem, Mohamed Atta, Wassif, Khaled Tawfik, Bayomi, Hanaa, Abdou, Sherif Mahdy
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 27-01-2024
Nature Publishing Group
Nature Portfolio
Subjects:
639/705
639/705/1046
639/705/117
639/705/258
Arabic language
Case studies
Datasets
Dialects
Humanities and Social Sciences
Literary translation
Machine learning
Machine translation
Monolingualism
multidisciplinary
Parallel corpora
Science
Science (multidisciplinary)
Standard dialects
Translation
Translations
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Summary:Machine translation for low-resource languages poses significant challenges, primarily due to the limited availability of data. In recent years, unsupervised learning has emerged as a promising approach to overcome this issue by aiming to learn translations between languages without depending on parallel data. A wide range of methods have been proposed in the literature to address this complex problem. This paper presents an in-depth investigation of semi-supervised neural machine translation specifically focusing on translating Arabic dialects, particularly Egyptian, to Modern Standard Arabic. The study employs two distinct datasets: one parallel dataset containing aligned sentences in both dialects, and a monolingual dataset where the source dialect is not directly connected to the target language in the training data. Three different translation systems are explored in this study. The first is an attention-based sequence-to-sequence model that benefits from the shared vocabulary between the Egyptian dialect and Modern Arabic to learn word embeddings. The second is an unsupervised transformer model that depends solely on monolingual data, without any parallel data. The third system starts with the parallel dataset for an initial supervised learning phase and then incorporates the monolingual data during the training process.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-51090-4