@inproceedings{mansour-etal-2026-sage,
title = "{SAGE} at {A}ra{G}enre 2026: Adapter-Refined Definition Embeddings for Zero-Shot {A}rabic Genre Classification",
author = "Mansour, Yasmina and
Ballout, Mohamad and
Afles, Ahmad",
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.36/",
pages = "288--294",
ISBN = "979-8-89176-497-2",
abstract = "We present our system SAGE: $\textbf{S}$entence $\textbf{A}$nalysis $\textbf{G}$enre $\textbf{E}$valuation for the AraGenre shared task, which assigns Arabic sentences a hierarchical genre label from an English definition alone, with none of the 74 evaluation genres represented in the official training data. Since a definition is not itself an instance of its genre, we fine-tune the encoder generating the embeddings of both the sentences to classify and the candidate genre definitions and train a small adapter that refines those of the definitions. Studying three encoders and four datasets that were generated for training purposes, we find that the adapter{'}s training data matters more than either encoder choice or adapter hidden layer. Our best system, combining a fine-tuned encoder and an adapter, reached a Hierarchical Macro F1 score of 0.5278, placing seventh."
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<abstract>We present our system SAGE: Sentence Analysis Genre Evaluation for the AraGenre shared task, which assigns Arabic sentences a hierarchical genre label from an English definition alone, with none of the 74 evaluation genres represented in the official training data. Since a definition is not itself an instance of its genre, we fine-tune the encoder generating the embeddings of both the sentences to classify and the candidate genre definitions and train a small adapter that refines those of the definitions. Studying three encoders and four datasets that were generated for training purposes, we find that the adapter’s training data matters more than either encoder choice or adapter hidden layer. Our best system, combining a fine-tuned encoder and an adapter, reached a Hierarchical Macro F1 score of 0.5278, placing seventh.</abstract>
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%0 Conference Proceedings
%T SAGE at AraGenre 2026: Adapter-Refined Definition Embeddings for Zero-Shot Arabic Genre Classification
%A Mansour, Yasmina
%A Ballout, Mohamad
%A Afles, Ahmad
%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 mansour-etal-2026-sage
%X We present our system SAGE: Sentence Analysis Genre Evaluation for the AraGenre shared task, which assigns Arabic sentences a hierarchical genre label from an English definition alone, with none of the 74 evaluation genres represented in the official training data. Since a definition is not itself an instance of its genre, we fine-tune the encoder generating the embeddings of both the sentences to classify and the candidate genre definitions and train a small adapter that refines those of the definitions. Studying three encoders and four datasets that were generated for training purposes, we find that the adapter’s training data matters more than either encoder choice or adapter hidden layer. Our best system, combining a fine-tuned encoder and an adapter, reached a Hierarchical Macro F1 score of 0.5278, placing seventh.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.36/
%P 288-294
Markdown (Informal)
[SAGE at AraGenre 2026: Adapter-Refined Definition Embeddings for Zero-Shot Arabic Genre Classification](https://aclanthology.org/2026.arabicnlp-sharedtasks.36/) (Mansour et al., ArabicNLP 2026)
ACL