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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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| 2 |
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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| 4 |
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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| 5 |
Contrastive Feature Typologies of Arabic Consonant Reflexes
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In: Languages ; Volume 6 ; Issue 3 (2021)
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| 6 |
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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| 7 |
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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| 8 |
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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| 9 |
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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| 10 |
Integrating Dialects and Dialectology in the Curriculum of Teaching Arabic As a Foreign Language (TAFL)
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| 11 |
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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| 12 |
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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| 13 |
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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| 15 |
Dataset construction for the detection of anti-social behaviour in online communication in arabic
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Abstract:
peer-reviewed ; Warning: this paper contains a range of words which may cause offence. In recent years, many studies target anti-social behaviour such as offensive language and cyberbullying in online communication. Typically, these studies collect data from various reachable sources, the majority of the datasets being in English. However, to the best of our knowledge, there is no dataset collected from the YouTube platform targeting Arabic text and overall there are only a few datasets of Arabic text, collected from other social platforms for the purpose of offensive language detection. Therefore, in this paper we contribute to this field by presenting a dataset of YouTube comments in Arabic, specifically designed to be used for the detection of offensive language in a machine learning scenario. Our dataset contains a range of offensive language and flaming in the form of YouTube comments. We document the labelling process we have conducted, taking into account the difference in the Arab dialects and the diversity of perception of offensive language throughout the Arab world. Furthermore, statistical analysis of the dataset is presented, in order to make it ready for use as a training dataset for predictive modelling.
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Keyword:
Anti-social behaviour online; Arabic dataset; Arabic dialects; harassment detection; offensive language; text classification; text mining
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URL: http://hdl.handle.net/10344/7878
https://doi.org/10.1016/j.procs.2018.10.473
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| 16 |
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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| 17 |
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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| 18 |
Faamugol Haala Ji Araabu Ji ; Understanding How to Speak Arabic
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| 19 |
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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