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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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| 24 |
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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| 25 |
Markers in urban Hijazi discourse ; Markers in urban Hijazi discoures
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Compliments and compliment responses in Saudi Arabic in text-based computer-mediated communication
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| 27 |
Gender differences in Saudi Arabic question formation on Twitter
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Integrating Dialects and Dialectology in the Curriculum of Teaching Arabic As a Foreign Language (TAFL)
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| 29 |
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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| 30 |
Durative aspect markers in modern Arabic dialects : cross-dialectal functions and historical development ...
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| 35 |
Automatic Identification of Maghreb Dialects Using a Dictionary-Based Approach
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In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) ; Eleventh International Conference on Language Resources and Evaluation (LREC 2018) ; https://hal.archives-ouvertes.fr/hal-02012150 ; Eleventh International Conference on Language Resources and Evaluation (LREC 2018), May 2018, Miyazaki, Japan (2018)
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| 36 |
Statistical Machine Translation: Application to low resourced languages ; Traduction Automatique Fondée sur des Méthodes Statistiques : Application aux Langues peu Dotées en Ressources
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In: https://hal.inria.fr/tel-03186940 ; Computation and Language [cs.CL]. École Supérieure d’Informatique, 2018. English (2018)
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| 37 |
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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| 38 |
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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