@inproceedings{ilesanmi-etal-2026-textling,
title = "{T}ext{L}ing at {A}r{G}uard Shared Tasks: An Enhanced {MARBERT}-Based Late-Fusion Model for Multimodal Hateful Meme Detection in {A}rabic",
author = "Ilesanmi, Emmanuel O. and
Adebanji, Olaronke Oluwayemisi and
Ogunlenu, Hamid T. and
Ojo, Olumide Ebenezer and
Calvo, Hiram and
Gelbukh, Alexander",
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.51/",
pages = "407--412",
ISBN = "979-8-89176-497-2",
abstract = "The classification task for ArGuard 2026 Track A, Subtask A1, identifies whether an Arabic meme is Hateful or Not Hateful. Our system combines three separately trained models. In the first experiment, we use word-level and character-level features with balanced linear classifiers. In the second experiment, we fine- tune MARBERT for text classification using class-balanced focal loss. In the third experi- ment, we use a frozen SigLIP image encoder with balanced logistic regression. We combine the Class-1 probabilities from the three mod- els using late fusion. On the test set, the fused model achieved a Macro-F1 score of 0.76 and an accuracy of 0.79"
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<abstract>The classification task for ArGuard 2026 Track A, Subtask A1, identifies whether an Arabic meme is Hateful or Not Hateful. Our system combines three separately trained models. In the first experiment, we use word-level and character-level features with balanced linear classifiers. In the second experiment, we fine- tune MARBERT for text classification using class-balanced focal loss. In the third experi- ment, we use a frozen SigLIP image encoder with balanced logistic regression. We combine the Class-1 probabilities from the three mod- els using late fusion. On the test set, the fused model achieved a Macro-F1 score of 0.76 and an accuracy of 0.79</abstract>
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%0 Conference Proceedings
%T TextLing at ArGuard Shared Tasks: An Enhanced MARBERT-Based Late-Fusion Model for Multimodal Hateful Meme Detection in Arabic
%A Ilesanmi, Emmanuel O.
%A Adebanji, Olaronke Oluwayemisi
%A Ogunlenu, Hamid T.
%A Ojo, Olumide Ebenezer
%A Calvo, Hiram
%A Gelbukh, Alexander
%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 ilesanmi-etal-2026-textling
%X The classification task for ArGuard 2026 Track A, Subtask A1, identifies whether an Arabic meme is Hateful or Not Hateful. Our system combines three separately trained models. In the first experiment, we use word-level and character-level features with balanced linear classifiers. In the second experiment, we fine- tune MARBERT for text classification using class-balanced focal loss. In the third experi- ment, we use a frozen SigLIP image encoder with balanced logistic regression. We combine the Class-1 probabilities from the three mod- els using late fusion. On the test set, the fused model achieved a Macro-F1 score of 0.76 and an accuracy of 0.79
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.51/
%P 407-412
Markdown (Informal)
[TextLing at ArGuard Shared Tasks: An Enhanced MARBERT-Based Late-Fusion Model for Multimodal Hateful Meme Detection in Arabic](https://aclanthology.org/2026.arabicnlp-sharedtasks.51/) (Ilesanmi et al., ArabicNLP 2026)
ACL