@inproceedings{adebanji-etal-2026-linglab,
title = "{L}ing{L}ab at {A}r{G}uard Shared Tasks: A Lightweight Hybrid Classifier for {A}rabic Harmful Prompt Detection",
author = "Adebanji, Olaronke Oluwayemisi and
Ilesanmi, Emmanuel O. and
Ojo, Olumide Ebenezer and
Calvo, Hiram and
Sidorov, Grigori and
Feldman, Anna",
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.49/",
pages = "394--399",
ISBN = "979-8-89176-497-2",
abstract = "The classification of Arabic prompts as Safe or Unsafe is the focus of this submission to ArGuard 2026 Subtask B1. We developed a lightweight hybrid classifier that combines lexical and semantic information. The lexical branch uses normalized word-level and character-level features with a class-balanced LinearSVC classifier. The semantic branch uses frozen multilingual E5 embeddings with a class-balanced logistic regression classifier. We combined the outputs of both branches using late fusion. On the development set, LinearSVC achieved a Macro-F1 score of 0.93, while the frozen E5 branch achieved 0.88. The final hybrid system achieved a Macro-F1 score of 0.93. On the test set, the submitted system achieved 0.78 accuracy, 0.83 precision, 0.70 recall, and 0.72 Macro-F1."
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<abstract>The classification of Arabic prompts as Safe or Unsafe is the focus of this submission to ArGuard 2026 Subtask B1. We developed a lightweight hybrid classifier that combines lexical and semantic information. The lexical branch uses normalized word-level and character-level features with a class-balanced LinearSVC classifier. The semantic branch uses frozen multilingual E5 embeddings with a class-balanced logistic regression classifier. We combined the outputs of both branches using late fusion. On the development set, LinearSVC achieved a Macro-F1 score of 0.93, while the frozen E5 branch achieved 0.88. The final hybrid system achieved a Macro-F1 score of 0.93. On the test set, the submitted system achieved 0.78 accuracy, 0.83 precision, 0.70 recall, and 0.72 Macro-F1.</abstract>
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%0 Conference Proceedings
%T LingLab at ArGuard Shared Tasks: A Lightweight Hybrid Classifier for Arabic Harmful Prompt Detection
%A Adebanji, Olaronke Oluwayemisi
%A Ilesanmi, Emmanuel O.
%A Ojo, Olumide Ebenezer
%A Calvo, Hiram
%A Sidorov, Grigori
%A Feldman, Anna
%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 adebanji-etal-2026-linglab
%X The classification of Arabic prompts as Safe or Unsafe is the focus of this submission to ArGuard 2026 Subtask B1. We developed a lightweight hybrid classifier that combines lexical and semantic information. The lexical branch uses normalized word-level and character-level features with a class-balanced LinearSVC classifier. The semantic branch uses frozen multilingual E5 embeddings with a class-balanced logistic regression classifier. We combined the outputs of both branches using late fusion. On the development set, LinearSVC achieved a Macro-F1 score of 0.93, while the frozen E5 branch achieved 0.88. The final hybrid system achieved a Macro-F1 score of 0.93. On the test set, the submitted system achieved 0.78 accuracy, 0.83 precision, 0.70 recall, and 0.72 Macro-F1.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.49/
%P 394-399
Markdown (Informal)
[LingLab at ArGuard Shared Tasks: A Lightweight Hybrid Classifier for Arabic Harmful Prompt Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.49/) (Adebanji et al., ArabicNLP 2026)
ACL