@inproceedings{ragab-etal-2019-mawdoo3,
title = "Mawdoo3 {AI} at {MADAR} Shared Task: {A}rabic Fine-Grained Dialect Identification with Ensemble Learning",
author = "Ragab, Ahmad and
Seelawi, Haitham and
Samir, Mostafa and
Mattar, Abdelrahman and
Al-Bataineh, Hesham and
Zaghloul, Mohammad and
Mustafa, Ahmad and
Talafha, Bashar and
Freihat, Abed Alhakim and
Al-Natsheh, Hussein",
editor = "El-Hajj, Wassim and
Belguith, Lamia Hadrich and
Bougares, Fethi and
Magdy, Walid and
Zitouni, Imed and
Tomeh, Nadi and
El-Haj, Mahmoud and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4630",
doi = "10.18653/v1/W19-4630",
pages = "244--248",
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.",
}
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%0 Conference Proceedings
%T Mawdoo3 AI at MADAR Shared Task: Arabic Fine-Grained Dialect Identification with Ensemble Learning
%A Ragab, Ahmad
%A Seelawi, Haitham
%A Samir, Mostafa
%A Mattar, Abdelrahman
%A Al-Bataineh, Hesham
%A Zaghloul, Mohammad
%A Mustafa, Ahmad
%A Talafha, Bashar
%A Freihat, Abed Alhakim
%A Al-Natsheh, Hussein
%Y El-Hajj, Wassim
%Y Belguith, Lamia Hadrich
%Y Bougares, Fethi
%Y Magdy, Walid
%Y Zitouni, Imed
%Y Tomeh, Nadi
%Y El-Haj, Mahmoud
%Y Zaghouani, Wajdi
%S Proceedings of the Fourth Arabic Natural Language Processing Workshop
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F ragab-etal-2019-mawdoo3
%X 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.
%R 10.18653/v1/W19-4630
%U https://aclanthology.org/W19-4630
%U https://doi.org/10.18653/v1/W19-4630
%P 244-248
Markdown (Informal)
[Mawdoo3 AI at MADAR Shared Task: Arabic Fine-Grained Dialect Identification with Ensemble Learning](https://aclanthology.org/W19-4630) (Ragab et al., WANLP 2019)
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.