@inproceedings{ahmad-etal-2026-nasaq,
title = "Nasaq at {A}ra{G}enre 2026: Definition-Guided Approaches for Hierarchical {A}rabic Genre Classification",
author = "Ahmad, Samar and
Alharbi, Abdullah I. and
Altamimi, Shahad and
Assiri, Yahya and
Alrajhi, Morooj",
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.35/",
pages = "281--287",
ISBN = "979-8-89176-497-2",
abstract = "We describe SynAnchor, Nasaq{'}s definition-guided system for AraGenre{~}2026. The task requires hierarchical Arabic genre classification under disjoint specific-genre inventories across training, development, and test, making generalization to unseen genres a central challenge. SynAnchor addresses this setting by generating Arabic anchor texts from genre definitions using GPT-4o and embedding them with BGE-M3 for similarity-based retrieval. High-confidence cases are classified directly, while ambiguous inputs are selectively adjudicated by GPT-5 among the three highest-ranked candidate genres. On the official test set of 27,972 texts spanning 74 unseen specific genres, SynAnchor achieves a Hierarchical Macro-F1 of 0.6006 on Codabench, compared with 0.2472 for the organizers' definition-embedding baseline (approximately 2.4). Approximately 25{\%} of test instances are resolved using retrieval alone, without LLM adjudication. The results suggest that combining definition-guided Arabic anchor synthesis with selective LLM reasoning can support hierarchical genre classification under changing label inventories."
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<abstract>We describe SynAnchor, Nasaq’s definition-guided system for AraGenre 2026. The task requires hierarchical Arabic genre classification under disjoint specific-genre inventories across training, development, and test, making generalization to unseen genres a central challenge. SynAnchor addresses this setting by generating Arabic anchor texts from genre definitions using GPT-4o and embedding them with BGE-M3 for similarity-based retrieval. High-confidence cases are classified directly, while ambiguous inputs are selectively adjudicated by GPT-5 among the three highest-ranked candidate genres. On the official test set of 27,972 texts spanning 74 unseen specific genres, SynAnchor achieves a Hierarchical Macro-F1 of 0.6006 on Codabench, compared with 0.2472 for the organizers’ definition-embedding baseline (approximately 2.4). Approximately 25% of test instances are resolved using retrieval alone, without LLM adjudication. The results suggest that combining definition-guided Arabic anchor synthesis with selective LLM reasoning can support hierarchical genre classification under changing label inventories.</abstract>
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%0 Conference Proceedings
%T Nasaq at AraGenre 2026: Definition-Guided Approaches for Hierarchical Arabic Genre Classification
%A Ahmad, Samar
%A Alharbi, Abdullah I.
%A Altamimi, Shahad
%A Assiri, Yahya
%A Alrajhi, Morooj
%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 ahmad-etal-2026-nasaq
%X We describe SynAnchor, Nasaq’s definition-guided system for AraGenre 2026. The task requires hierarchical Arabic genre classification under disjoint specific-genre inventories across training, development, and test, making generalization to unseen genres a central challenge. SynAnchor addresses this setting by generating Arabic anchor texts from genre definitions using GPT-4o and embedding them with BGE-M3 for similarity-based retrieval. High-confidence cases are classified directly, while ambiguous inputs are selectively adjudicated by GPT-5 among the three highest-ranked candidate genres. On the official test set of 27,972 texts spanning 74 unseen specific genres, SynAnchor achieves a Hierarchical Macro-F1 of 0.6006 on Codabench, compared with 0.2472 for the organizers’ definition-embedding baseline (approximately 2.4). Approximately 25% of test instances are resolved using retrieval alone, without LLM adjudication. The results suggest that combining definition-guided Arabic anchor synthesis with selective LLM reasoning can support hierarchical genre classification under changing label inventories.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.35/
%P 281-287
Markdown (Informal)
[Nasaq at AraGenre 2026: Definition-Guided Approaches for Hierarchical Arabic Genre Classification](https://aclanthology.org/2026.arabicnlp-sharedtasks.35/) (Ahmad et al., ArabicNLP 2026)
ACL