@inproceedings{sibaee-etal-2026-namaa,
title = "{NAMAA} Community at the {BAREC} Shared Task 2026: Ordinal-Aware Training for Fine-Grained {A}rabic Readability Assessment Beyond Architectural Scale",
author = "Sibaee, Serry and
Al Jallad, Khloud and
Elhosiny, Israa and
Mohamed Emad Elden, Fatimah and
Nacar, Omer",
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.23/",
pages = "198--208",
ISBN = "979-8-89176-497-2",
abstract = "Fine-grained Arabic readability assessment is challenging due to the language{'}s rich morphology and the ordered nature of readability labels. We investigate this problem in the sentence-level Strict Track of the BAREC Shared Task 2026, which uses a 19-level readability scale. We evaluate six controlled configurations spanning engineered linguistic and morphological features, TF-IDF/SVD lexical representations, contextual transformer encoders, multi-encoder fusion, output-level ensembling, and ordinal-aware training, followed by a final refinement using a single EmbeddingGemma encoder. Contextual transformer representations provide the largest improvement over classical feature-based baselines, while multi-encoder fusion and voting yield smaller additional gains. In contrast, word-repetition and adjacent-word-swap augmentation degrades performance, indicating that these perturbations do not reliably preserve readability labels. Our strongest configuration combines EmbeddingGemma with Gaussian soft labels, KL-divergence training, expected-value decoding, class weighting, and best-checkpoint selection, achieving 84.0 QWK on the test set and 82.4 QWK on the official blind test set. Overall, the results suggest that explicitly accounting for the ordinal structure of readability labels is a promising alternative to simply increasing architectural or ensemble complexity. We further summarize the resulting design insights into a practical framework for Arabic ordinal readability modeling."
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<abstract>Fine-grained Arabic readability assessment is challenging due to the language’s rich morphology and the ordered nature of readability labels. We investigate this problem in the sentence-level Strict Track of the BAREC Shared Task 2026, which uses a 19-level readability scale. We evaluate six controlled configurations spanning engineered linguistic and morphological features, TF-IDF/SVD lexical representations, contextual transformer encoders, multi-encoder fusion, output-level ensembling, and ordinal-aware training, followed by a final refinement using a single EmbeddingGemma encoder. Contextual transformer representations provide the largest improvement over classical feature-based baselines, while multi-encoder fusion and voting yield smaller additional gains. In contrast, word-repetition and adjacent-word-swap augmentation degrades performance, indicating that these perturbations do not reliably preserve readability labels. Our strongest configuration combines EmbeddingGemma with Gaussian soft labels, KL-divergence training, expected-value decoding, class weighting, and best-checkpoint selection, achieving 84.0 QWK on the test set and 82.4 QWK on the official blind test set. Overall, the results suggest that explicitly accounting for the ordinal structure of readability labels is a promising alternative to simply increasing architectural or ensemble complexity. We further summarize the resulting design insights into a practical framework for Arabic ordinal readability modeling.</abstract>
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%0 Conference Proceedings
%T NAMAA Community at the BAREC Shared Task 2026: Ordinal-Aware Training for Fine-Grained Arabic Readability Assessment Beyond Architectural Scale
%A Sibaee, Serry
%A Al Jallad, Khloud
%A Elhosiny, Israa
%A Mohamed Emad Elden, Fatimah
%A Nacar, Omer
%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 sibaee-etal-2026-namaa
%X Fine-grained Arabic readability assessment is challenging due to the language’s rich morphology and the ordered nature of readability labels. We investigate this problem in the sentence-level Strict Track of the BAREC Shared Task 2026, which uses a 19-level readability scale. We evaluate six controlled configurations spanning engineered linguistic and morphological features, TF-IDF/SVD lexical representations, contextual transformer encoders, multi-encoder fusion, output-level ensembling, and ordinal-aware training, followed by a final refinement using a single EmbeddingGemma encoder. Contextual transformer representations provide the largest improvement over classical feature-based baselines, while multi-encoder fusion and voting yield smaller additional gains. In contrast, word-repetition and adjacent-word-swap augmentation degrades performance, indicating that these perturbations do not reliably preserve readability labels. Our strongest configuration combines EmbeddingGemma with Gaussian soft labels, KL-divergence training, expected-value decoding, class weighting, and best-checkpoint selection, achieving 84.0 QWK on the test set and 82.4 QWK on the official blind test set. Overall, the results suggest that explicitly accounting for the ordinal structure of readability labels is a promising alternative to simply increasing architectural or ensemble complexity. We further summarize the resulting design insights into a practical framework for Arabic ordinal readability modeling.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.23/
%P 198-208
Markdown (Informal)
[NAMAA Community at the BAREC Shared Task 2026: Ordinal-Aware Training for Fine-Grained Arabic Readability Assessment Beyond Architectural Scale](https://aclanthology.org/2026.arabicnlp-sharedtasks.23/) (Sibaee et al., ArabicNLP 2026)
ACL