@inproceedings{alsulieman-2026-razan,
title = "Razan Alsulieman at {S}tance{E}val-2026: Validation Under Target Shift for {A}rabic Stance Detection",
author = "Alsulieman, Razan",
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.67/",
pages = "519--523",
ISBN = "979-8-89176-497-2",
abstract = "We describe our systems for both StanceEval- 2026 Arabic stance-detection tracks. Our MARBERT-centered pipeline optimized the primary macro-F1 over AGAINST and FAVOR directly, combining target-aware fine-tuning, probability ensembles, and guarded transfer experiments. We report one official test sub- mission per track. The Track 1 target-routed MARBERT system scored 0.8925 on devel- opment and 0.6743 on the official test set. The Track 2 MARBERT/Fanar/Qwen3 system scored 0.8360 on development and 0.8100 on the official test set. The released Track 1 evaluation input contained only Women Driv- ing, absent from its labeled train and develop- ment targets, making the nominal seen-target track an effective target-transfer test for our system. These results show that validation pro- tocols must reflect the intended target-transfer setting; otherwise, same-target development scores can substantially overstate transfer per- formance."
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<abstract>We describe our systems for both StanceEval- 2026 Arabic stance-detection tracks. Our MARBERT-centered pipeline optimized the primary macro-F1 over AGAINST and FAVOR directly, combining target-aware fine-tuning, probability ensembles, and guarded transfer experiments. We report one official test sub- mission per track. The Track 1 target-routed MARBERT system scored 0.8925 on devel- opment and 0.6743 on the official test set. The Track 2 MARBERT/Fanar/Qwen3 system scored 0.8360 on development and 0.8100 on the official test set. The released Track 1 evaluation input contained only Women Driv- ing, absent from its labeled train and develop- ment targets, making the nominal seen-target track an effective target-transfer test for our system. These results show that validation pro- tocols must reflect the intended target-transfer setting; otherwise, same-target development scores can substantially overstate transfer per- formance.</abstract>
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%0 Conference Proceedings
%T Razan Alsulieman at StanceEval-2026: Validation Under Target Shift for Arabic Stance Detection
%A Alsulieman, Razan
%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 alsulieman-2026-razan
%X We describe our systems for both StanceEval- 2026 Arabic stance-detection tracks. Our MARBERT-centered pipeline optimized the primary macro-F1 over AGAINST and FAVOR directly, combining target-aware fine-tuning, probability ensembles, and guarded transfer experiments. We report one official test sub- mission per track. The Track 1 target-routed MARBERT system scored 0.8925 on devel- opment and 0.6743 on the official test set. The Track 2 MARBERT/Fanar/Qwen3 system scored 0.8360 on development and 0.8100 on the official test set. The released Track 1 evaluation input contained only Women Driv- ing, absent from its labeled train and develop- ment targets, making the nominal seen-target track an effective target-transfer test for our system. These results show that validation pro- tocols must reflect the intended target-transfer setting; otherwise, same-target development scores can substantially overstate transfer per- formance.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.67/
%P 519-523
Markdown (Informal)
[Razan Alsulieman at StanceEval-2026: Validation Under Target Shift for Arabic Stance Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.67/) (Alsulieman, ArabicNLP 2026)
ACL