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

SZED公布ArGuard 2026任务的阿拉伯语仇恨表情包检测方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

SZED的研究人员详细介绍了他们为ArGuard 2026共享任务设计的阿拉伯语仇恨表情包检测方法。他们提交的二元分类（A1）方案结合了MARBERTv2和CLIP ViT-L/14的嵌入，取得了0.7597的宏观F1分数。对于细粒度的多标签标记任务（A2），他们采用了多任务标签注意力架构，在10个类别上实现了0.3142的平均F1分数。 AI

影响这项研究有助于开发用于识别在线仇恨内容的工具，特别是在阿拉伯语领域。

排序理由该项目是一篇研究论文，详细介绍了对共享任务的提交。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Selma Khelili, Zohra Abdeli · 2026-10-21 04:00

SZED参加ArGuard共享任务：多任务标签注意力与跨模态融合用于阿拉伯语仇恨表情包检测

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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报道来源 [1]

SZED参加ArGuard共享任务：多任务标签注意力与跨模态融合用于阿拉伯语仇恨表情包检测

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