Problem
Research questions and friction points this paper is trying to address.
Arabic genre classification
low-resource languages
annotated data
hierarchical classification
definition-guided
AraGenre 2026: A Hierarchical Definition-Guided Arabic Genre Classification Shared Task
🤖 AI Summary
本文通过定义引导的方法解决阿拉伯语文体分类问题，采用层级分类法应对标注数据有限的情况，并通过共享任务评估不同系统的性能。
📝 Abstract
AraGenre is a shared task on hierarchical, definition-guided Arabic genre classification, motivated by the limited availability of annotated data in Arabic and other low-resource languages. Systems assign each Arabic text segment both a broad communicative genre and a fine-grained specific genre. The released training and development sets contain limited, primarily synthetic and controlled examples, whereas the hidden final benchmark contains noisier naturally occurring text spanning Modern Standard Arabic, Classical Arabic, and multiple dialects. Participants received natural-language definitions for 74 previously unseen specific genres, creating a zero-shot label generalisation setting in which systems had to infer class semantics rather than memorise fixed label-feature associations. The task attracted 46 registrations and 373 submissions, with 17 teams completing the final evaluation. Thakaa ranked first with a Hierarchical Macro F1 of 0.7352, followed by HoangPhong (HP) with 0.7169 and NAMAA with 0.7013. The results show strong broad-genre recognition but a substantial gap in fine-grained classification under linguistic and domain variation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
hierarchical definition-guided
zero-shot label generalisation
Arabic genre classification
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