@inproceedings{verma-etal-2026-ttlab,
title = "{TTL}ab at {S}tance{E}val-2026: A Cloze-Style Prompting Approach for {A}rabic-Language Stance Detection ({CLASP}-Ar)",
author = "Verma, Bhuvanesh and
Abusaleh, Ali and
Mehler, Alexander",
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.66/",
pages = "511--518",
ISBN = "979-8-89176-497-2",
abstract = "Arabic-language stance detection remains challenging, and previous shared-task systems have largely relied on multitask learning and ensembles. While these systems achieve state-of-the-art performance, their applicability and transferability are limited by the additional complexity introduced by multitask learning. To reduce this complexity, we introduce $\texttt{CLASP-Ar}$, which reformulates the task as cloze-style masked language modeling. In this approach, the target, predicted sentiment, and text are combined into a single prompt whose $\texttt{[MASK]}$ prediction is restricted to a verbalizer-constrained label vocabulary. When evaluated on the StanceEval-2026: Arabic Stance Detection Shared Task, our proposed approach achieves an $F_{\text{avg2}}$ score of 71.36{\%} on Track 1 and 74.14{\%} on Track 2 test sets."
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<abstract>Arabic-language stance detection remains challenging, and previous shared-task systems have largely relied on multitask learning and ensembles. While these systems achieve state-of-the-art performance, their applicability and transferability are limited by the additional complexity introduced by multitask learning. To reduce this complexity, we introduce CLASP-Ar, which reformulates the task as cloze-style masked language modeling. In this approach, the target, predicted sentiment, and text are combined into a single prompt whose [MASK] prediction is restricted to a verbalizer-constrained label vocabulary. When evaluated on the StanceEval-2026: Arabic Stance Detection Shared Task, our proposed approach achieves an F_\textavg2 score of 71.36% on Track 1 and 74.14% on Track 2 test sets.</abstract>
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%0 Conference Proceedings
%T TTLab at StanceEval-2026: A Cloze-Style Prompting Approach for Arabic-Language Stance Detection (CLASP-Ar)
%A Verma, Bhuvanesh
%A Abusaleh, Ali
%A Mehler, Alexander
%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 verma-etal-2026-ttlab
%X Arabic-language stance detection remains challenging, and previous shared-task systems have largely relied on multitask learning and ensembles. While these systems achieve state-of-the-art performance, their applicability and transferability are limited by the additional complexity introduced by multitask learning. To reduce this complexity, we introduce CLASP-Ar, which reformulates the task as cloze-style masked language modeling. In this approach, the target, predicted sentiment, and text are combined into a single prompt whose [MASK] prediction is restricted to a verbalizer-constrained label vocabulary. When evaluated on the StanceEval-2026: Arabic Stance Detection Shared Task, our proposed approach achieves an F_\textavg2 score of 71.36% on Track 1 and 74.14% on Track 2 test sets.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.66/
%P 511-518
Markdown (Informal)
[TTLab at StanceEval-2026: A Cloze-Style Prompting Approach for Arabic-Language Stance Detection (CLASP-Ar)](https://aclanthology.org/2026.arabicnlp-sharedtasks.66/) (Verma et al., ArabicNLP 2026)
ACL