@inproceedings{laskar-laskar-2026-bahash,
title = "Bahash-{AI} at {A}r{G}uard Shared Tasks: Multimodal Ensemble Learning for Hateful Meme Detection",
author = "Laskar, Ahmadullah and
Laskar, Sahinur Rahman",
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.55/",
pages = "432--436",
ISBN = "979-8-89176-497-2",
abstract = "Hateful content in memes is difficult to detect automatically, as harmful intent often emerges from the interaction between image and text rather than either modality alone, a challenge compounded in Arabic by dialectal variation and sarcasm. This paper presents the system submitted by team Bahash-AI to Subtask A1 (binary hateful meme detection) of the ArGuard shared task at ArabicNLP 2026. A late-fusion classifier combining MARBERTv2 and ViT-base is first developed and evaluated as a standalone development-phase system. Building on this, the final submission ensembles a CLIP-based text-image classifier with this same late-fusion classifier, retrained on the combined train, development, and development-test partitions. Class imbalance (approximately 30{\%} Hateful) is addressed via class-weighted losses, oversampling, and threshold selection. The final ensemble achieves a Macro-F1 of 0.7457 and an accuracy of 0.7680 on the blind test set, ranking 12th of 16 teams on the official leaderboard and outperforming the development-phase system by 9 Macro-F1 points. Error analysis using per-class precision and recall shows that the two systems reach a similar Hateful-class F1 gap through opposite error patterns."
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<abstract>Hateful content in memes is difficult to detect automatically, as harmful intent often emerges from the interaction between image and text rather than either modality alone, a challenge compounded in Arabic by dialectal variation and sarcasm. This paper presents the system submitted by team Bahash-AI to Subtask A1 (binary hateful meme detection) of the ArGuard shared task at ArabicNLP 2026. A late-fusion classifier combining MARBERTv2 and ViT-base is first developed and evaluated as a standalone development-phase system. Building on this, the final submission ensembles a CLIP-based text-image classifier with this same late-fusion classifier, retrained on the combined train, development, and development-test partitions. Class imbalance (approximately 30% Hateful) is addressed via class-weighted losses, oversampling, and threshold selection. The final ensemble achieves a Macro-F1 of 0.7457 and an accuracy of 0.7680 on the blind test set, ranking 12th of 16 teams on the official leaderboard and outperforming the development-phase system by 9 Macro-F1 points. Error analysis using per-class precision and recall shows that the two systems reach a similar Hateful-class F1 gap through opposite error patterns.</abstract>
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%0 Conference Proceedings
%T Bahash-AI at ArGuard Shared Tasks: Multimodal Ensemble Learning for Hateful Meme Detection
%A Laskar, Ahmadullah
%A Laskar, Sahinur Rahman
%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 laskar-laskar-2026-bahash
%X Hateful content in memes is difficult to detect automatically, as harmful intent often emerges from the interaction between image and text rather than either modality alone, a challenge compounded in Arabic by dialectal variation and sarcasm. This paper presents the system submitted by team Bahash-AI to Subtask A1 (binary hateful meme detection) of the ArGuard shared task at ArabicNLP 2026. A late-fusion classifier combining MARBERTv2 and ViT-base is first developed and evaluated as a standalone development-phase system. Building on this, the final submission ensembles a CLIP-based text-image classifier with this same late-fusion classifier, retrained on the combined train, development, and development-test partitions. Class imbalance (approximately 30% Hateful) is addressed via class-weighted losses, oversampling, and threshold selection. The final ensemble achieves a Macro-F1 of 0.7457 and an accuracy of 0.7680 on the blind test set, ranking 12th of 16 teams on the official leaderboard and outperforming the development-phase system by 9 Macro-F1 points. Error analysis using per-class precision and recall shows that the two systems reach a similar Hateful-class F1 gap through opposite error patterns.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.55/
%P 432-436
Markdown (Informal)
[Bahash-AI at ArGuard Shared Tasks: Multimodal Ensemble Learning for Hateful Meme Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.55/) (Laskar & Laskar, ArabicNLP 2026)
ACL