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

arXiv:2609.29733v1 Announce Type: new 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 exttt{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 exttt{[MASK]} prediction is restricted to a verbalizer-constrained label vocabulary.

Source: arXiv cs.CL | 2026-09-25