Mawdoo3 AI at MADAR Shared Task: Arabic Fine-Grained Dialect Identification with Ensemble Learning

Ahmad Ragab, Haitham Seelawi, Mostafa Samir, Abdelrahman Mattar, Hesham Al-Bataineh, Mohammad Zaghloul, Ahmad Mustafa, Bashar Talafha, Abed Alhakim Freihat, Hussein Al-Natsheh

Abstract
In this paper we discuss several models we used to classify 25 city-level Arabic dialects in addition to Modern Standard Arabic (MSA) as part of MADAR shared task (sub-task 1). We propose an ensemble model of a group of experimentally designed best performing classifiers on a various set of features. Our system achieves an accuracy of 69.3% macro F1-score with an improvement of 1.4% accuracy from the baseline model on the DEV dataset. Our best run submitted model ranked as third out of 19 participating teams on the TEST dataset with only 0.12% macro F1-score behind the top ranked system.
Anthology ID:
W19-4630
Volume:
Proceedings of the Fourth Arabic Natural Language Processing Workshop
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Wassim El-Hajj, Lamia Hadrich Belguith, Fethi Bougares, Walid Magdy, Imed Zitouni, Nadi Tomeh, Mahmoud El-Haj, Wajdi Zaghouani
Venue:
WANLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
244–248
Language:
URL:
https://aclanthology.org/W19-4630
DOI:
10.18653/v1/W19-4630
Bibkey:
Cite (ACL):
Ahmad Ragab, Haitham Seelawi, Mostafa Samir, Abdelrahman Mattar, Hesham Al-Bataineh, Mohammad Zaghloul, Ahmad Mustafa, Bashar Talafha, Abed Alhakim Freihat, and Hussein Al-Natsheh. 2019. Mawdoo3 AI at MADAR Shared Task: Arabic Fine-Grained Dialect Identification with Ensemble Learning. In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 244–248, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Mawdoo3 AI at MADAR Shared Task: Arabic Fine-Grained Dialect Identification with Ensemble Learning (Ragab et al., WANLP 2019)
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https://preview.aclanthology.org/naacl-24-ws-corrections/W19-4630.pdf
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