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SZED details Arabic hateful meme detection approach for ArGuard 2026 tasks

By PulseAugur Editorial · [1 sources] ·

Researchers from SZED have detailed their approach for the ArGuard 2026 shared tasks focused on detecting Arabic hateful memes. Their submission for binary classification (A1) combined MARBERTv2 and CLIP ViT-L/14 embeddings, achieving a macro-F1 score of 0.7597. For the fine-grained multi-label tagging task (A2), they employed a multi-task label-attention architecture, resulting in an average F1 score of 0.3142 across 10 classes. AI

IMPACT This research contributes to the development of tools for identifying hateful content online, particularly in Arabic.

RANK_REASON The item is a research paper detailing a submission to a shared task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SZED details Arabic hateful meme detection approach for ArGuard 2026 tasks

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The item is a research paper detailing a submission to a shared task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Selma Khelili, Zohra Abdeli ·

    SZED at ArGuard Shared Tasks: Multi-Task Label-Attention and Cross-Modal Fusion for Arabic Hateful Meme Detection

    arXiv:2610.26972v1 Announce Type: cross Abstract: We present SZED's submission to ArGuard 2026 Track A: Arabic hateful meme detection. The task has two parts: binary classification (A1) and fine-grained multi-label tagging (A2). For A1, we fuse MARBERTv2 and CLIP ViT-L/14 embeddi…

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