@inproceedings{aloufi-etal-2026-tarjih,
title = "Tarjih at {S}tance{E}val-2026: Argument-Guided {A}rabic Stance Detection",
author = "Aloufi, Samah and
Alrefaei, Joud Ibrahim and
Alharbi, Rafaa Faisal and
Alsaedi, Wejdan Ahmed and
Almarwani, Nada",
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.69/",
pages = "529--537",
ISBN = "979-8-89176-497-2",
abstract = "We present Tarjih, our proposed system for the Stance Detection in Arabic Language shared task (StanceEval-2026). Rather than predicting stance directly, Tarjih formulates the task as verification among competing arguments. An instruction-tuned LLM generates one steelman argument for each stance label, and a second blind pass evaluates each argument against the tweet without access to the gold label. Each argument{--}tweet is then jointly encoded with AraBERT to model their compatibility. From this pipeline, we derive three systems: a gated classifier, a pair scorer, and a ranking read-out. The pair scorer achieves the best seen-target test Favg2 of 0.73, while the gated classifier performs best on the unseen-target track with 0.77. Overall, the lower test performance and ranking among the submitted systems indicate limited generalization across targets."
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<abstract>We present Tarjih, our proposed system for the Stance Detection in Arabic Language shared task (StanceEval-2026). Rather than predicting stance directly, Tarjih formulates the task as verification among competing arguments. An instruction-tuned LLM generates one steelman argument for each stance label, and a second blind pass evaluates each argument against the tweet without access to the gold label. Each argument–tweet is then jointly encoded with AraBERT to model their compatibility. From this pipeline, we derive three systems: a gated classifier, a pair scorer, and a ranking read-out. The pair scorer achieves the best seen-target test Favg2 of 0.73, while the gated classifier performs best on the unseen-target track with 0.77. Overall, the lower test performance and ranking among the submitted systems indicate limited generalization across targets.</abstract>
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%0 Conference Proceedings
%T Tarjih at StanceEval-2026: Argument-Guided Arabic Stance Detection
%A Aloufi, Samah
%A Alrefaei, Joud Ibrahim
%A Alharbi, Rafaa Faisal
%A Alsaedi, Wejdan Ahmed
%A Almarwani, Nada
%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 aloufi-etal-2026-tarjih
%X We present Tarjih, our proposed system for the Stance Detection in Arabic Language shared task (StanceEval-2026). Rather than predicting stance directly, Tarjih formulates the task as verification among competing arguments. An instruction-tuned LLM generates one steelman argument for each stance label, and a second blind pass evaluates each argument against the tweet without access to the gold label. Each argument–tweet is then jointly encoded with AraBERT to model their compatibility. From this pipeline, we derive three systems: a gated classifier, a pair scorer, and a ranking read-out. The pair scorer achieves the best seen-target test Favg2 of 0.73, while the gated classifier performs best on the unseen-target track with 0.77. Overall, the lower test performance and ranking among the submitted systems indicate limited generalization across targets.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.69/
%P 529-537
Markdown (Informal)
[Tarjih at StanceEval-2026: Argument-Guided Arabic Stance Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.69/) (Aloufi et al., ArabicNLP 2026)
ACL
- Samah Aloufi, Joud Ibrahim Alrefaei, Rafaa Faisal Alharbi, Wejdan Ahmed Alsaedi, and Nada Almarwani. 2026. Tarjih at StanceEval-2026: Argument-Guided Arabic Stance Detection. In Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks, pages 529–537, Budapest, Hungary. Association for Computational Linguistics.