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Neural machine translation for Arabic dialects
Thesis (MSc)--Stellenbosch University, 2024.
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| Format: | Thesis |
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Stellenbosch : Stellenbosch University
2025
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| _version_ | 1867613900624625664 |
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| access_status_str | Open Access |
| author | Salim, Aya Hashim Taha |
| author2 | Brink, Willie |
| author_browse | Brink, Willie Salim, Aya Hashim Taha |
| author_facet | Brink, Willie Salim, Aya Hashim Taha |
| author_sort | Salim, Aya Hashim Taha |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description |
Thesis (MSc)--Stellenbosch University, 2024. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/131902 |
| institution | Stellenbosch University (South Africa) |
| last_indexed | 2026-06-10T12:43:29.841Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/131902 Neural machine translation for Arabic dialects Salim, Aya Hashim Taha Brink, Willie Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Applied Mathematics Division. Arabic language -- Machine translating Natural language processing (Computer science) Neural networks (Computer science) UCTD Thesis (MSc)--Stellenbosch University, 2024. ENGLISH ABSTRACT: We explore two approaches to improve machine translation for low-resource Arabic dialects: unsupervised domain adaptation and backtranslation. Arabic dialects exhibit distinct linguistic features, with some dialects being more similar to each other than others. Leveraging this characteristic, we demonstrate that a model trained on one group of dialects can effectively translate other dialects without additional labelled data. This approach leads to improved translation quality for all dialects and reduces the gap between distinct and similar dialects. Our proposed methodology involves initially training a neural machine translation model on various dialects using parallel corpora. Subsequently, fine-tuning is performed on unlabelled data of another dialect, where the translation model is jointly trained with an unsupervised domain adaptation discriminator. We also show that backtranslation can improve the performance of a base model, by generating synthetic parallel data and selecting sentences similar in domain to those in the existing parallel corpus. Domain cosine and domain fine-tune methods are deployed using different language models, to select data from the generated parallel data. Finally, we show that a multi-dialect model utilizing both unsupervised domain adaptation and backtranslation can outperform all other versions of our models and also those from the literature. AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar. Masters 2025-04-08T09:19:04Z 2025-04-08T09:19:04Z 2024-12 Thesis https://scholar.sun.ac.za/handle/10019.1/131902 Stellenbosch University viii, 79 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Arabic language -- Machine translating Natural language processing (Computer science) Neural networks (Computer science) UCTD Salim, Aya Hashim Taha Neural machine translation for Arabic dialects |
| title | Neural machine translation for Arabic dialects |
| title_full | Neural machine translation for Arabic dialects |
| title_fullStr | Neural machine translation for Arabic dialects |
| title_full_unstemmed | Neural machine translation for Arabic dialects |
| title_short | Neural machine translation for Arabic dialects |
| title_sort | neural machine translation for arabic dialects |
| topic | Arabic language -- Machine translating Natural language processing (Computer science) Neural networks (Computer science) UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/131902 |
| work_keys_str_mv | AT salimayahashimtaha neuralmachinetranslationforarabicdialects |