AraGenre 2026：一个层次化定义引导的阿拉伯语体裁分类共享任务

arXiv cs.CL 论文
arabic-nlp genre-classification shared-task low-resource-language hierarchical-classification zero-shot-learning

摘要

AraGenre 2026 是一个旨在改进低资源语言标注数据的层次化定义引导阿拉伯语体裁分类共享任务。结果表明，其在宽泛体裁识别方面表现强劲，但在细粒度分类上存在差距。

arXiv:2609.27387v1 Announce Type: new 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.
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# AraGenre 2026: A Hierarchical Definition-Guided Arabic Genre Classification Shared Task
Source: [https://arxiv.org/abs/2609.27387](https://arxiv.org/abs/2609.27387)
[View PDF](https://arxiv.org/pdf/2609.27387)

> 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\.

## Submission history

From: Mo El\-Haj \[[view email](https://arxiv.org/show-email/2333adf4/2609.27387)\] **\[v1\]**Wed, 23 Sep 2026 05:39:45 UTC \(795 KB\)

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