@inproceedings{alnashri-alshanqiti-2026-nabra,
title = "Nabra at {S}tance{E}val-2026: Exploring Fine-Tuning Strategies for {A}rabic Stance Detection",
author = "Alnashri, Lama and
Alshanqiti, Elham",
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.77/",
pages = "613--616",
ISBN = "979-8-89176-497-2",
abstract = "We present Nabra, a system submitted to StanceEval-2026 Track 1 for Arabic stance detection. Our approach fine-tunes MARBERTv2 on the Mawqif-v2 dataset, exploring seven configurations including class weighting, regularization, data augmentation via back-translation, resampling strategies, and zero-shot inference with Qwen2.5-7B. Our best model by the primary metric Favg2 is M1 (Baseline), achieving 0.837 on the development set and 0.6704 on the official blind test set. Error analysis reveals that Favor{\textrightarrow}Against misclassification accounts for 84.6{\%} of errors, driven by negative sentiment words co-occurring with supportive stances. We find that metric choice (Favg2 vs. Favg3) significantly impacts model selection decisions."
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<abstract>We present Nabra, a system submitted to StanceEval-2026 Track 1 for Arabic stance detection. Our approach fine-tunes MARBERTv2 on the Mawqif-v2 dataset, exploring seven configurations including class weighting, regularization, data augmentation via back-translation, resampling strategies, and zero-shot inference with Qwen2.5-7B. Our best model by the primary metric Favg2 is M1 (Baseline), achieving 0.837 on the development set and 0.6704 on the official blind test set. Error analysis reveals that Favor→Against misclassification accounts for 84.6% of errors, driven by negative sentiment words co-occurring with supportive stances. We find that metric choice (Favg2 vs. Favg3) significantly impacts model selection decisions.</abstract>
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%0 Conference Proceedings
%T Nabra at StanceEval-2026: Exploring Fine-Tuning Strategies for Arabic Stance Detection
%A Alnashri, Lama
%A Alshanqiti, Elham
%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 alnashri-alshanqiti-2026-nabra
%X We present Nabra, a system submitted to StanceEval-2026 Track 1 for Arabic stance detection. Our approach fine-tunes MARBERTv2 on the Mawqif-v2 dataset, exploring seven configurations including class weighting, regularization, data augmentation via back-translation, resampling strategies, and zero-shot inference with Qwen2.5-7B. Our best model by the primary metric Favg2 is M1 (Baseline), achieving 0.837 on the development set and 0.6704 on the official blind test set. Error analysis reveals that Favor→Against misclassification accounts for 84.6% of errors, driven by negative sentiment words co-occurring with supportive stances. We find that metric choice (Favg2 vs. Favg3) significantly impacts model selection decisions.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.77/
%P 613-616
Markdown (Informal)
[Nabra at StanceEval-2026: Exploring Fine-Tuning Strategies for Arabic Stance Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.77/) (Alnashri & Alshanqiti, ArabicNLP 2026)
ACL