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Towards a Dialect History of the Baggara Belt
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In: EISSN: 2226-471X ; Languages ; https://hal.archives-ouvertes.fr/hal-03334787 ; Languages, MDPI, 2021, 6 (3), pp.146 (1-17). ⟨10.3390/languages6030146⟩ (2021)
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| 2 |
On Interdental Fricatives in the First-Layer Dialects of Maghrebi Arabic
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In: ISSN: 1876-6633 ; Brill's Journal of Afroasiatic Languages and Linguistics ; https://hal.archives-ouvertes.fr/hal-03465827 ; Brill's Journal of Afroasiatic Languages and Linguistics, Brill, 2021 ; https://brill.com/view/journals/aall/13/2/article-p288_5.xml (2021)
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| 3 |
Improving Machine Translation of Arabic Dialects through Multi-Task Learning
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In: 20th International Conference Italian Association for Artificial Intelligence:AIxIA 2021 ; https://hal.archives-ouvertes.fr/hal-03435996 ; 20th International Conference Italian Association for Artificial Intelligence:AIxIA 2021, Dec 2021, MILAN/Virtual, Italy (2021)
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| 5 |
Towards a Dialect History of the Baggara Belt
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In: EISSN: 2226-471X ; Languages ; https://hal.archives-ouvertes.fr/hal-03334787 ; Languages, MDPI, 2021, 6 (3), pp.146 (1-17). ⟨10.3390/languages6030146⟩ (2021)
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| 6 |
A Historical Reconstruction of Some Pronominal Suffixes in Modern Dialectal Arabic
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In: Languages ; Volume 6 ; Issue 3 (2021)
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| 7 |
A Transformer-Based Neural Machine Translation Model for Arabic Dialects That Utilizes Subword Units
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In: Sensors ; Volume 21 ; Issue 19 (2021)
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Abstract:
Languages that allow free word order, such as Arabic dialects, are of significant difficulty for neural machine translation (NMT) because of many scarce words and the inefficiency of NMT systems to translate these words. Unknown Word (UNK) tokens represent the out-of-vocabulary words for the reason that NMT systems run with vocabulary that has fixed size. Scarce words are encoded completely as sequences of subword pieces employing the Word-Piece Model. This research paper introduces the first Transformer-based neural machine translation model for Arabic vernaculars that employs subword units. The proposed solution is based on the Transformer model that has been presented lately. The use of subword units and shared vocabulary within the Arabic dialect (the source language) and modern standard Arabic (the target language) enhances the behavior of the multi-head attention sublayers for the encoder by obtaining the overall dependencies between words of input sentence for Arabic vernacular. Experiments are carried out from Levantine Arabic vernacular (LEV) to modern standard Arabic (MSA) and Maghrebi Arabic vernacular (MAG) to MSA, Gulf–MSA, Nile–MSA, Iraqi Arabic (IRQ) to MSA translation tasks. Extensive experiments confirm that the suggested model adequately addresses the unknown word issue and boosts the quality of translation from Arabic vernaculars to Modern standard Arabic (MSA).
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Keyword:
Arabic dialects; modern standard Arabic; multi-head attention; neural machine translation (NMT); self-attention; shared vocabulary; subword units; transformer
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URL: https://doi.org/10.3390/s21196509
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| 8 |
The Old and the New: Considerations in Arabic Historical Dialectology
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In: Languages ; Volume 6 ; Issue 4 (2021)
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| 9 |
Contrastive Feature Typologies of Arabic Consonant Reflexes
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In: Languages ; Volume 6 ; Issue 3 (2021)
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| 10 |
The Southern Moroccan Dialects and the Hilā ; li Category
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In: Languages ; Volume 6 ; Issue 4 (2021)
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| 11 |
Mahdia Dialect: An Urban Vernacular in the Tunisian Sahel Context
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In: Languages ; Volume 6 ; Issue 3 (2021)
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| 12 |
Edición y traducción del texto ḥassāní manuscrito de la primera constitución mauritana ; Edition and translation of the Ḥassāní manuscript of the first Mauritanian constitution
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| 13 |
The production and perception of peripheral geminate/singleton coronal stop contrasts in Arabic
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