@inproceedings{zaina-etal-2026-beyond,
title = "Beyond Gender, Number, and Rationality: Modeling Extended {A}rabic Morpho-Syntactic Agreement with Aggregation",
author = "Zaina, Bielasan and
Khalifa, Salam and
Habash, Nizar",
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",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-main.7/",
pages = "77--87",
ISBN = "979-8-89176-473-6",
abstract = "Arabic gender{--}number morpho-syntactic agreement exhibits systematic variation beyond functional gender, number, and rationality (GNR) (Alkuhlani and Habash, 2011). We identify and classify agreement patterns involving plural-to-unit and unit-to-members shifts, and introduce {\_}aggregation{\_} (AGG), a lexeme-level feature and corpus-derived lexical-semantic resource that captures these extended agreement affordances. We extend the GNR agreement framework with aggregation and evaluate it on over one million agreement pairs from ATB1/2/3 and BAREC. AGG improves agreement prediction over a strong baseline by 1.4{\%} absolute on average (38{\%} error reduction). Remaining errors largely reflect analyzer, parser, and treebank limitations. Interpretable agreement assessment of this kind can support the development of pedagogical applications and benchmarking the grammaticality of generative model outputs.[Data and Code are publicly available: https://github.com/CAMeL-Lab/Arabic{\_}GNRA{\_}Lexicon.]"
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<abstract>Arabic gender–number morpho-syntactic agreement exhibits systematic variation beyond functional gender, number, and rationality (GNR) (Alkuhlani and Habash, 2011). We identify and classify agreement patterns involving plural-to-unit and unit-to-members shifts, and introduce _aggregation_ (AGG), a lexeme-level feature and corpus-derived lexical-semantic resource that captures these extended agreement affordances. We extend the GNR agreement framework with aggregation and evaluate it on over one million agreement pairs from ATB1/2/3 and BAREC. AGG improves agreement prediction over a strong baseline by 1.4% absolute on average (38% error reduction). Remaining errors largely reflect analyzer, parser, and treebank limitations. Interpretable agreement assessment of this kind can support the development of pedagogical applications and benchmarking the grammaticality of generative model outputs.[Data and Code are publicly available: https://github.com/CAMeL-Lab/Arabic_GNRA_Lexicon.]</abstract>
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%0 Conference Proceedings
%T Beyond Gender, Number, and Rationality: Modeling Extended Arabic Morpho-Syntactic Agreement with Aggregation
%A Zaina, Bielasan
%A Khalifa, Salam
%A Habash, Nizar
%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
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-473-6
%F zaina-etal-2026-beyond
%X Arabic gender–number morpho-syntactic agreement exhibits systematic variation beyond functional gender, number, and rationality (GNR) (Alkuhlani and Habash, 2011). We identify and classify agreement patterns involving plural-to-unit and unit-to-members shifts, and introduce _aggregation_ (AGG), a lexeme-level feature and corpus-derived lexical-semantic resource that captures these extended agreement affordances. We extend the GNR agreement framework with aggregation and evaluate it on over one million agreement pairs from ATB1/2/3 and BAREC. AGG improves agreement prediction over a strong baseline by 1.4% absolute on average (38% error reduction). Remaining errors largely reflect analyzer, parser, and treebank limitations. Interpretable agreement assessment of this kind can support the development of pedagogical applications and benchmarking the grammaticality of generative model outputs.[Data and Code are publicly available: https://github.com/CAMeL-Lab/Arabic_GNRA_Lexicon.]
%U https://aclanthology.org/2026.arabicnlp-main.7/
%P 77-87
Markdown (Informal)
[Beyond Gender, Number, and Rationality: Modeling Extended Arabic Morpho-Syntactic Agreement with Aggregation](https://aclanthology.org/2026.arabicnlp-main.7/) (Zaina et al., ArabicNLP 2026)
ACL