@inproceedings{jamal-etal-2022-arabic,
title = "On The {A}rabic Dialects{'} Identification: Overcoming Challenges of Geographical Similarities Between {A}rabic dialects and Imbalanced Datasets",
author = "Jamal, Salma and
.Kassem, Aly M and
Mohamed, Omar and
Ashraf, Ali",
editor = "Bouamor, Houda and
Al-Khalifa, Hend and
Darwish, Kareem and
Rambow, Owen and
Bougares, Fethi and
Abdelali, Ahmed and
Tomeh, Nadi and
Khalifa, Salam and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wanlp-1.49",
doi = "10.18653/v1/2022.wanlp-1.49",
pages = "458--463",
abstract = "Arabic is one of the world{'}s richest languages, with a diverse range of dialects based on geographical origin. In this paper, we present a solution to tackle subtask 1 (Country-level dialect identification) of the Nuanced Arabic Dialect Identification (NADI) shared task 2022 achieving third place with an average macro F1 score between the two test sets of 26.44{\%}. In the preprocessing stage, we removed the most common frequent terms from all sentences across all dialects, and in the modeling step, we employed a hybrid loss function approach that includes Weighted cross entropy loss and Vector Scaling(VS) Loss. On test sets A and B, our model achieved 35.68{\%} and 17.192{\%} Macro F1 scores, respectively.",
}
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<abstract>Arabic is one of the world’s richest languages, with a diverse range of dialects based on geographical origin. In this paper, we present a solution to tackle subtask 1 (Country-level dialect identification) of the Nuanced Arabic Dialect Identification (NADI) shared task 2022 achieving third place with an average macro F1 score between the two test sets of 26.44%. In the preprocessing stage, we removed the most common frequent terms from all sentences across all dialects, and in the modeling step, we employed a hybrid loss function approach that includes Weighted cross entropy loss and Vector Scaling(VS) Loss. On test sets A and B, our model achieved 35.68% and 17.192% Macro F1 scores, respectively.</abstract>
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%0 Conference Proceedings
%T On The Arabic Dialects’ Identification: Overcoming Challenges of Geographical Similarities Between Arabic dialects and Imbalanced Datasets
%A Jamal, Salma
%A .Kassem, Aly M.
%A Mohamed, Omar
%A Ashraf, Ali
%Y Bouamor, Houda
%Y Al-Khalifa, Hend
%Y Darwish, Kareem
%Y Rambow, Owen
%Y Bougares, Fethi
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Khalifa, Salam
%Y Zaghouani, Wajdi
%S Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F jamal-etal-2022-arabic
%X Arabic is one of the world’s richest languages, with a diverse range of dialects based on geographical origin. In this paper, we present a solution to tackle subtask 1 (Country-level dialect identification) of the Nuanced Arabic Dialect Identification (NADI) shared task 2022 achieving third place with an average macro F1 score between the two test sets of 26.44%. In the preprocessing stage, we removed the most common frequent terms from all sentences across all dialects, and in the modeling step, we employed a hybrid loss function approach that includes Weighted cross entropy loss and Vector Scaling(VS) Loss. On test sets A and B, our model achieved 35.68% and 17.192% Macro F1 scores, respectively.
%R 10.18653/v1/2022.wanlp-1.49
%U https://aclanthology.org/2022.wanlp-1.49
%U https://doi.org/10.18653/v1/2022.wanlp-1.49
%P 458-463
Markdown (Informal)
[On The Arabic Dialects’ Identification: Overcoming Challenges of Geographical Similarities Between Arabic dialects and Imbalanced Datasets](https://aclanthology.org/2022.wanlp-1.49) (Jamal et al., WANLP 2022)
ACL