@inproceedings{al-qasem-2026-rabee,
title = "Rabee Al-Qasem at {A}ra{G}enre 2026: Conformer Based Hierarchical Classifier for {A}rabic Genres",
author = "Al-Qasem, Rabee",
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.28/",
pages = "246--251",
ISBN = "979-8-89176-497-2",
abstract = "This paper presents the first investigation into the performance of the Conformer architecture for hierarchical text classification, specifically applied to predicting two-level Arabic text genres. The proposed system combines multilingual sentence embeddings, target label definition representations, a Conformer projection module, an auxiliary broad-genre classifier, synthetic data augmentation, and seed-based ensembling. Evaluated on the official shared task, the approach achieved a Hierarchical Macro-F1 of 0.4399, demonstrating strong high-level genre classification (Broad Macro-F1 of 0.6288) while highlighting ongoing challenges in distinguishing semantically close specific subcategories (Specific Macro-F1 of 0.2511)."
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%0 Conference Proceedings
%T Rabee Al-Qasem at AraGenre 2026: Conformer Based Hierarchical Classifier for Arabic Genres
%A Al-Qasem, Rabee
%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 al-qasem-2026-rabee
%X This paper presents the first investigation into the performance of the Conformer architecture for hierarchical text classification, specifically applied to predicting two-level Arabic text genres. The proposed system combines multilingual sentence embeddings, target label definition representations, a Conformer projection module, an auxiliary broad-genre classifier, synthetic data augmentation, and seed-based ensembling. Evaluated on the official shared task, the approach achieved a Hierarchical Macro-F1 of 0.4399, demonstrating strong high-level genre classification (Broad Macro-F1 of 0.6288) while highlighting ongoing challenges in distinguishing semantically close specific subcategories (Specific Macro-F1 of 0.2511).
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.28/
%P 246-251
Markdown (Informal)
[Rabee Al-Qasem at AraGenre 2026: Conformer Based Hierarchical Classifier for Arabic Genres](https://aclanthology.org/2026.arabicnlp-sharedtasks.28/) (Al-Qasem, ArabicNLP 2026)
ACL