TTLab at StanceEval-2026: A Cloze-Style Prompting Approach for Arabic-Language Stance Detection (CLASP-Ar)

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 this http URL 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.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.29733 [cs.CL]

(or arXiv:2609.29733v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.29733 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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