@inproceedings{hossain-2026-axiom,
title = "Axiom at {A}r{G}uard Shared Tasks: Unimodal Text Dominance for {A}rabic Hate Meme Detection",
author = "Hossain, Md. Ajwad",
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.50/",
pages = "400--406",
ISBN = "979-8-89176-497-2",
abstract = "Detecting hate speech in social media memes requires disentangling complex cross-modal interactions, a challenge magnified in low-resource and morphologically rich languages like Arabic. We present Team Axiom{'}s submission to the ArGuard 2026 Shared Task (Task A1) on binary hate meme classification. While conventional multimodal wisdom posits that text and visual features must be jointly fused, empirical evidence across non-English domains reveals that noisy visual backbones and cross-modal space misalignment frequently degrade language signals. We validate this hypothesis by fine-tuning a unimodal MARBERTv2 sequence classifier without external multimodal fusion. Our simple, well-regularized approach achieved a Macro-F1 of 0.770 and an accuracy of 0.818 on the official gold test set, securing 6th place on the global leaderboard and outperforming both the official AraBERTv2 baseline and our own complex multimodal fusion architectures."
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<abstract>Detecting hate speech in social media memes requires disentangling complex cross-modal interactions, a challenge magnified in low-resource and morphologically rich languages like Arabic. We present Team Axiom’s submission to the ArGuard 2026 Shared Task (Task A1) on binary hate meme classification. While conventional multimodal wisdom posits that text and visual features must be jointly fused, empirical evidence across non-English domains reveals that noisy visual backbones and cross-modal space misalignment frequently degrade language signals. We validate this hypothesis by fine-tuning a unimodal MARBERTv2 sequence classifier without external multimodal fusion. Our simple, well-regularized approach achieved a Macro-F1 of 0.770 and an accuracy of 0.818 on the official gold test set, securing 6th place on the global leaderboard and outperforming both the official AraBERTv2 baseline and our own complex multimodal fusion architectures.</abstract>
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%0 Conference Proceedings
%T Axiom at ArGuard Shared Tasks: Unimodal Text Dominance for Arabic Hate Meme Detection
%A Hossain, Md. Ajwad
%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 hossain-2026-axiom
%X Detecting hate speech in social media memes requires disentangling complex cross-modal interactions, a challenge magnified in low-resource and morphologically rich languages like Arabic. We present Team Axiom’s submission to the ArGuard 2026 Shared Task (Task A1) on binary hate meme classification. While conventional multimodal wisdom posits that text and visual features must be jointly fused, empirical evidence across non-English domains reveals that noisy visual backbones and cross-modal space misalignment frequently degrade language signals. We validate this hypothesis by fine-tuning a unimodal MARBERTv2 sequence classifier without external multimodal fusion. Our simple, well-regularized approach achieved a Macro-F1 of 0.770 and an accuracy of 0.818 on the official gold test set, securing 6th place on the global leaderboard and outperforming both the official AraBERTv2 baseline and our own complex multimodal fusion architectures.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.50/
%P 400-406
Markdown (Informal)
[Axiom at ArGuard Shared Tasks: Unimodal Text Dominance for Arabic Hate Meme Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.50/) (Hossain, ArabicNLP 2026)
ACL