发表机构
Text Technology Lab (TTLab); Goethe University Frankfurt（文本技术实验室（TTLab）; 法兰克福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对阿拉伯语立场检测，提出CLASP-Ar方法，通过完形填空式提示和掩码语言建模简化任务，降低多任务学习的复杂性，同时保持高性能。
AI 中文摘要
阿拉伯语立场检测仍然具有挑战性，以往的共享任务系统主要依赖多任务学习和集成方法。尽管这些系统达到了最先进的性能，但其适用性和可迁移性受到多任务学习引入的额外复杂性的限制。为了降低这种复杂性，我们引入了CLASP-Ar，它将任务重新表述为完形填空式掩码语言建模。在该方法中，目标、预测情感和文本被组合成一个单一提示，其[MASK]预测被限制在由言语化器约束的标签词汇表内。
英文摘要
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.
CommentsAccepted at ArabicNLP 2026 StanceEval-2026 shared task
