@inproceedings{zaman-rishta-2026-nile,
title = "Nile Nexus at {BAREC} Shared Task 2026: Enhancing Fine-Grained {A}rabic Readability Assessment through Ordinal-Aware Transformer Ensembling and Decision Calibration",
author = "Zaman, Sumaiya and
Rishta, Miftahul Jannat",
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.20/",
pages = "179--185",
ISBN = "979-8-89176-497-2",
abstract = "Readability assessment has played a vital role in education, yet most NLP work has focused on English and a few high-resource languages, leaving fine-grained Arabic readability comparatively unexplored. In the BAREC Shared Task 2026, we have addressed sentence-level classification into 19 ordinal readability levels under the Strict Track, fine-tuning four Arabic transformer encoders, MARBERT, CAMeLBERT MSA, AraELECTRA and AraBERTv2, on the official training split under multiple ordinal-aware losses: soft cross-entropy, squared Earth Mover{'}s Distance, direct ordinal regression and a CORAL-style cumulative-threshold loss. A validation-only greedy ensemble of all resulting models, combined with a learned per-level threshold post-processing step, has raised performance from a best single-model QWK of 0.8341 to a final validation QWK of 0.8507. On the official Blind Test leaderboard, our system has achieved a QWK of 0.848, securing 3rd place in the Strict Track"
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<abstract>Readability assessment has played a vital role in education, yet most NLP work has focused on English and a few high-resource languages, leaving fine-grained Arabic readability comparatively unexplored. In the BAREC Shared Task 2026, we have addressed sentence-level classification into 19 ordinal readability levels under the Strict Track, fine-tuning four Arabic transformer encoders, MARBERT, CAMeLBERT MSA, AraELECTRA and AraBERTv2, on the official training split under multiple ordinal-aware losses: soft cross-entropy, squared Earth Mover’s Distance, direct ordinal regression and a CORAL-style cumulative-threshold loss. A validation-only greedy ensemble of all resulting models, combined with a learned per-level threshold post-processing step, has raised performance from a best single-model QWK of 0.8341 to a final validation QWK of 0.8507. On the official Blind Test leaderboard, our system has achieved a QWK of 0.848, securing 3rd place in the Strict Track</abstract>
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%0 Conference Proceedings
%T Nile Nexus at BAREC Shared Task 2026: Enhancing Fine-Grained Arabic Readability Assessment through Ordinal-Aware Transformer Ensembling and Decision Calibration
%A Zaman, Sumaiya
%A Rishta, Miftahul Jannat
%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 zaman-rishta-2026-nile
%X Readability assessment has played a vital role in education, yet most NLP work has focused on English and a few high-resource languages, leaving fine-grained Arabic readability comparatively unexplored. In the BAREC Shared Task 2026, we have addressed sentence-level classification into 19 ordinal readability levels under the Strict Track, fine-tuning four Arabic transformer encoders, MARBERT, CAMeLBERT MSA, AraELECTRA and AraBERTv2, on the official training split under multiple ordinal-aware losses: soft cross-entropy, squared Earth Mover’s Distance, direct ordinal regression and a CORAL-style cumulative-threshold loss. A validation-only greedy ensemble of all resulting models, combined with a learned per-level threshold post-processing step, has raised performance from a best single-model QWK of 0.8341 to a final validation QWK of 0.8507. On the official Blind Test leaderboard, our system has achieved a QWK of 0.848, securing 3rd place in the Strict Track
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.20/
%P 179-185
Markdown (Informal)
[Nile Nexus at BAREC Shared Task 2026: Enhancing Fine-Grained Arabic Readability Assessment through Ordinal-Aware Transformer Ensembling and Decision Calibration](https://aclanthology.org/2026.arabicnlp-sharedtasks.20/) (Zaman & Rishta, ArabicNLP 2026)
ACL