We present an overview of the ArAIEval shared task, organized as part of the first ArabicNLP 2023 conference co-located with EMNLP 2023. ArAIEval offers two tasks over Arabic text: (i) persuasion technique detection, focusing on identifying persuasion techniques in tweets and news articles, and (ii) disinformation detection in binary and multiclass setups over tweets. A total of 20 teams participated in the final evaluation phase, with 14 and 16 teams participating in Tasks 1 and 2, respectively. 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 give a brief overview of the participating systems. All datasets and evaluation scripts from the shared task 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ArAIEval共享任务，该任务作为首届阿拉伯自然语言处理2023大会（与EMNLP 2023联合举办）的一部分而组织。ArAIEval针对阿拉伯语文本提供两项任务：（i）说服技巧检测，侧重于识别推文和新闻文章中的说服技巧；（ii）虚假信息检测，涵盖推文的二分类与多分类设置。最终评估阶段共有20支团队参与，其中分别有14支和16支团队参与任务1和任务2。在这两项任务中，我们观察到微调Transformer模型（如AraBERT）是大多数参与系统的核心方法。本文描述了任务设置，包括数据集构建与评估设置的说明，并进一步简要概述了参与系统。该共享任务的所有数据集和评估脚本均已向研究社区发布。(https://araieval.gitlab.io/) 我们期待这将推动阿拉伯语中这些重要任务的进一步研究。