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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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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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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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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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Contrastive Feature Typologies of Arabic Consonant Reflexes
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In: Languages ; Volume 6 ; Issue 3 (2021)
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Vowel unpredictability in Hijazi Arabic monosyllabic verbs
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In: Glossa: a journal of general linguistics; Vol 5, No 1 (2020); 32 ; 2397-1835 (2020)
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Automatic identification methods on a corpus of twenty five fine-grained Arabic dialects
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In: Arabic Language Processing: From Theory to Practice7th International Conference, ICALP 2019, Nancy, France, October 16–17, 2019, Proceedings ; https://hal.archives-ouvertes.fr/hal-02314245 ; Arabic Language Processing: From Theory to Practice 7th International Conference, ICALP 2019, Nancy, France, October 16–17, 2019, Proceedings, Communications in Computer and Information Science book series (CCIS, volume 1108), 2019, ⟨10.1007/978-3-030-32959-4_6⟩ (2019)
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The SMarT Classifier for Arabic Fine-Grained Dialect Identification
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In: MADAR Shared Task: Arabic Fine-Grained Dialect Identification Dialect identification campaign ; The Fourth Arabic Natural Language Processing Workshop co-located with ACL ; https://hal.archives-ouvertes.fr/hal-02166384 ; The Fourth Arabic Natural Language Processing Workshop co-located with ACL, Aug 2019, Florence, Italy (2019)
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Script Independent Morphological Segmentation for Arabic Maghrebi Dialects: An Application to Machine Translation
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In: ISSN: 1405-5546 ; EISSN: 2007-9737 ; Computación y sistemas ; https://hal.archives-ouvertes.fr/hal-02274533 ; Computación y sistemas, Instituto Politécnico Nacional IPN Centro de Investigación en Computación, In press, 23 (3), pp.979-989. ⟨10.13053/cys-23-3-3267⟩ (2019)
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Integrating Dialects and Dialectology in the Curriculum of Teaching Arabic As a Foreign Language (TAFL)
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The phonology and micro-typology of Arabic R
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In: Glossa: a journal of general linguistics; Vol 4, No 1 (2019); 131 ; 2397-1835 (2019)
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La communication entre Libanais et Jordaniens sur les réseaux numériques ; Communication Practices Between Lebanese and Jordanians on Digital Networks
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In: Hermès [ISSN 0767-9513], Nouvelles voix de la recherche en communication, 2018, 82, p. 216 (2018)
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A Multitask-Based Neural Machine Translation Model with Part-of-Speech Tags Integration for Arabic Dialects
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In: Applied Sciences ; Volume 8 ; Issue 12 (2018)
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Abstract:
The statistical machine translation for the Arabic language integrates external linguistic resources such as part-of-speech tags. The current research presents a Bidirectional Long Short-Term Memory (Bi-LSTM) - Conditional Random Fields (CRF) segment-level Arabic Dialect POS tagger model, which will be integrated into the Multitask Neural Machine Translation (NMT) model. The proposed solution for NMT is based on the recurrent neural network encoder-decoder NMT model that has been introduced recently. The study has proposed and developed a unified Multitask NMT model that shares an encoder between the two tasks ; Arabic Dialect (AD) to Modern Standard Arabic (MSA) translation task and the segment-level POS tagging tasks. A shared layer and an invariant layer are shared between the translation tasks. By training translation tasks and POS tagging task alternately, the proposed model can leverage the characteristic information and improve the translation quality from Arabic dialects to Modern Standard Arabic. The experiments are conducted from Levantine Arabic (LA) to MSA and Maghrebi Arabic (MA) to MSA translation tasks. As an additional linguistic resource, the segment-level part-of-speech tags for Arabic dialects were also exploited. Experiments suggest that translation quality and the performance of POS tagger were improved with the implementation of multitask learning approach.
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Keyword:
Arabic dialects; Bi-LSTM; CRF; decoder; encoder; MSA; MTL; NMT; POS tagging
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URL: https://doi.org/10.3390/app8122502
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Dataset construction for the detection of anti-social behaviour in online communication in arabic
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Creating Parallel Arabic Dialect Corpus: Pitfalls to Avoid
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In: 18th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING) ; https://hal.archives-ouvertes.fr/hal-01557405 ; 18th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING), Apr 2017, Budapest, Hungary (2017)
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Proceedings of the International Conference on Natural Language Processing, Signal and Speech Processing
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In: https://hal.archives-ouvertes.fr/hal-03349724 ; 2017, 978-9954-99-758-1 (2017)
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Faamugol Haala Ji Araabu Ji ; Understanding How to Speak Arabic
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Quel arabe pour demain ? Les derniers avatars d'une controverse millénaire
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In: L'arabe moderne : Péripéties et enjeux ; https://halshs.archives-ouvertes.fr/halshs-01970199 ; Nejmeddine Khalfallah. L'arabe moderne : Péripéties et enjeux, Harmattan, 2015, 978-2-343-0490-52 (2015)
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