@inproceedings{ogunlenu-etal-2026-compling,
title = "{C}omp{L}ing at {A}r{G}uard Shared Tasks: Dialect-Aware Sparse Fusion for {A}rabic Prompt Safety Classification",
author = "Ogunlenu, Hamid T. and
Adebanji, Olaronke Oluwayemisi and
Ilesanmi, Emmanuel O. and
Ojo, Olumide Ebenezer and
Calvo, Hiram and
Sidorov, Grigori",
editor = "Abdul-Mageed, Muhammad and
Elsayed, Tamer and
Bouamor, Houda and
El-Beltagy, Samhaa and
Al-Khalifa, Hend and
Abdelali, Ahmed and
Elmadany, AbdelRahim and
El Mekki, Abdellah and
Darwish, Kareem and
Alkhereyf, Sakhar and
Luqman, Hamzah and
Khalifa, Salam and
Magdy, Samar and
Alyafeai, Zaid and
Hamed, Injy and
Zaghouani, Wajdi and
Zitouni, Imed",
booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference: Shared Tasks",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-sharedtasks.54/",
pages = "426--431",
ISBN = "979-8-89176-497-2",
abstract = "We present our approach in this submission for ArGuard 2026 Subtask B1, which classifies Arabic prompts directed at large language models as Safe or Unsafe. Our system combines four sparse TF-IDF feature representations with dialect indicators and surface-level features. We trained logistic regression and Naive-Bayes SVM (NBSVM) classifiers and combined their predictions using late fusion. The system achieved a Macro-F1 score of 0.94 and an accuracy of 0.96 on the development set. On the test set, our submission achieved 0.79 accuracy, 0.85 macro precision, 0.71 macro recall, and 0.72 Macro-F1."
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<abstract>We present our approach in this submission for ArGuard 2026 Subtask B1, which classifies Arabic prompts directed at large language models as Safe or Unsafe. Our system combines four sparse TF-IDF feature representations with dialect indicators and surface-level features. We trained logistic regression and Naive-Bayes SVM (NBSVM) classifiers and combined their predictions using late fusion. The system achieved a Macro-F1 score of 0.94 and an accuracy of 0.96 on the development set. On the test set, our submission achieved 0.79 accuracy, 0.85 macro precision, 0.71 macro recall, and 0.72 Macro-F1.</abstract>
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%0 Conference Proceedings
%T CompLing at ArGuard Shared Tasks: Dialect-Aware Sparse Fusion for Arabic Prompt Safety Classification
%A Ogunlenu, Hamid T.
%A Adebanji, Olaronke Oluwayemisi
%A Ilesanmi, Emmanuel O.
%A Ojo, Olumide Ebenezer
%A Calvo, Hiram
%A Sidorov, Grigori
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-497-2
%F ogunlenu-etal-2026-compling
%X We present our approach in this submission for ArGuard 2026 Subtask B1, which classifies Arabic prompts directed at large language models as Safe or Unsafe. Our system combines four sparse TF-IDF feature representations with dialect indicators and surface-level features. We trained logistic regression and Naive-Bayes SVM (NBSVM) classifiers and combined their predictions using late fusion. The system achieved a Macro-F1 score of 0.94 and an accuracy of 0.96 on the development set. On the test set, our submission achieved 0.79 accuracy, 0.85 macro precision, 0.71 macro recall, and 0.72 Macro-F1.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.54/
%P 426-431
Markdown (Informal)
[CompLing at ArGuard Shared Tasks: Dialect-Aware Sparse Fusion for Arabic Prompt Safety Classification](https://aclanthology.org/2026.arabicnlp-sharedtasks.54/) (Ogunlenu et al., ArabicNLP 2026)
ACL