We present an overview of the second edition of the ArAIEval shared task, organized as part of the ArabicNLP 2024 conference co-located with ACL 2024. In this edition, ArAIEval offers two tasks: (i) detection of propagandistic textual spans with persuasion techniques identification in tweets and news articles, and (ii) distinguishing between propagandistic and non-propagandistic memes. A total of 14 teams participated in the final evaluation phase, with 6 and 9 teams participating in Tasks 1 and 2, respectively. Finally, 11 teams submitted system description papers. Across both tasks, we observed that fine-tuning transformer models such as AraBERT was at the core of the majority of the participating systems. We provide a description of the task setup, including a description of the dataset construction and the evaluation setup. We further provide a brief overview of the participating systems. All datasets and evaluation scripts are released to the research community (https://araieval.gitlab.io/). We hope this will enable further research on these important tasks in Arabic.

翻译：本文概述了第二届ArAIEval共享任务，该任务作为与ACL 2024联合举办的ArabicNLP 2024会议的一部分而组织。在本届任务中，ArAIEval提供了两项子任务：(i) 在推文和新闻文章中检测具有说服技巧的宣传性文本片段，并识别其说服技巧；(ii) 区分宣传性与非宣传性网络迷因。共有14支队伍参与了最终评估阶段，其中分别有6支和9支队伍参与了任务1和任务2。最终，有11支队伍提交了系统描述论文。在两项任务中，我们观察到，对诸如AraBERT等Transformer模型进行微调是大多数参赛系统的核心方法。我们提供了任务设置的描述，包括数据集构建和评估设置的说明。我们还简要概述了参赛系统。所有数据集和评估脚本均已向研究社区发布（https://araieval.gitlab.io/）。我们希望这将推动针对阿拉伯语中这些重要任务的进一步研究。