发表机构
KFUPM; SDAIA-KFUPM JRC for AI; University of Tabuk（法赫德国王石油与矿业大学; 沙特数据与人工智能局-法赫德国王石油与矿业大学人工智能联合研究中心; 塔布克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Mawqif-v2阿拉伯语基准数据集，含996条标注推文，用于评估跨目标立场检测的模型泛化能力，并建立基线结果以支持可复现评估。
AI 中文摘要
公开可用的面向特定目标的阿拉伯语立场检测数据集仍然有限，尤其是用于评估跨目标泛化能力的数据集。本文介绍了Mawqif-v2扩展数据集，包含996条手动标注的阿拉伯语推文，这些推文采集自三个公开目标：女性驾驶、电动汽车和 trimester系统。每条推文都按照原始Mawqif标注方案标注了立场、情感和反讽标签。发布的扩展数据集旨在作为保留的评估集，用于评估模型对语义相关目标和先前未见目标的泛化能力，而原始Mawqif数据集则用于训练和开发。此外，我们使用多个阿拉伯语和多语言transformer模型以及零样本大语言模型（LLMs）建立了基线结果，以促进可复现的评估。结合原始Mawqif数据集，Mawqif-v2扩展数据集为评估阿拉伯语立场检测中的跨目标泛化能力提供了基准。
英文摘要
Publicly available Arabic datasets for target-specific stance detection remain limited, particularly for evaluating cross-target generalization. This paper presents the Mawqif-XT, consisting of 996 manually annotated Arabic tweets collected from three public targets: Women Driving, E-Cars, and Trimester System. Each tweet is annotated with stance, sentiment, and sarcasm labels following the original Mawqif annotation scheme. The released extension is intended as a held-out evaluation set for assessing model generalization to both semantically related and previously unseen targets, while the original Mawqif dataset is used for training and development. In addition, we establish baseline results using several Arabic and multilingual transformer models, as well as zero-shot large language models (LLMs), to facilitate reproducible evaluation. Together with the original Mawqif dataset, the Mawqif-v2 Extension provides a benchmark for evaluating cross-target generalization in Arabic stance detection.
