@inproceedings{khang-etal-2026-viper,
title = "Viper at {BAREC} Shared Task 2026: Structured {A}rabic Representations and Robust Multi-Seed Ensembling for Readability Assessment",
author = "Khang, Nguyen Minh and
Nho, Nguyen Ngoc and
Le, Trong Thanh and
V{\u{a}}n, Th{\`i}n {\DJ}ặng",
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.21/",
pages = "186--190",
ISBN = "979-8-89176-497-2",
abstract = "Fine-grained Arabic readability assessment aims to estimate how difficult an Arabic sentence is for its intended readers by assigning it to one of 19 ordered readability levels. This task is particularly challenging because of Arabic{'}s morphological complexity and the imbalanced distribution of examples across the levels. To address these challenges, we develop a system for the Sentence-Level Strict Track of the BAREC Shared Task 2026. Using only the official BAREC data, the system combines D3Tok-based morphological information, normalized surface text, and complementary features within a multi-seed ensemble of AraBERTv2-based regression models. Our submission achieved a QWK of 84.1{\%} on the official Blind Test. This result indicates that the proposed combination provides competitive performance for fine-grained Arabic readability assessment."
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<abstract>Fine-grained Arabic readability assessment aims to estimate how difficult an Arabic sentence is for its intended readers by assigning it to one of 19 ordered readability levels. This task is particularly challenging because of Arabic’s morphological complexity and the imbalanced distribution of examples across the levels. To address these challenges, we develop a system for the Sentence-Level Strict Track of the BAREC Shared Task 2026. Using only the official BAREC data, the system combines D3Tok-based morphological information, normalized surface text, and complementary features within a multi-seed ensemble of AraBERTv2-based regression models. Our submission achieved a QWK of 84.1% on the official Blind Test. This result indicates that the proposed combination provides competitive performance for fine-grained Arabic readability assessment.</abstract>
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%0 Conference Proceedings
%T Viper at BAREC Shared Task 2026: Structured Arabic Representations and Robust Multi-Seed Ensembling for Readability Assessment
%A Khang, Nguyen Minh
%A Nho, Nguyen Ngoc
%A Le, Trong Thanh
%A Văn, Thìn Đặng
%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 khang-etal-2026-viper
%X Fine-grained Arabic readability assessment aims to estimate how difficult an Arabic sentence is for its intended readers by assigning it to one of 19 ordered readability levels. This task is particularly challenging because of Arabic’s morphological complexity and the imbalanced distribution of examples across the levels. To address these challenges, we develop a system for the Sentence-Level Strict Track of the BAREC Shared Task 2026. Using only the official BAREC data, the system combines D3Tok-based morphological information, normalized surface text, and complementary features within a multi-seed ensemble of AraBERTv2-based regression models. Our submission achieved a QWK of 84.1% on the official Blind Test. This result indicates that the proposed combination provides competitive performance for fine-grained Arabic readability assessment.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.21/
%P 186-190
Markdown (Informal)
[Viper at BAREC Shared Task 2026: Structured Arabic Representations and Robust Multi-Seed Ensembling for Readability Assessment](https://aclanthology.org/2026.arabicnlp-sharedtasks.21/) (Khang et al., ArabicNLP 2026)
ACL