In this paper, we highlight our approach for the "Arabic AI Tasks Evaluation
(ArAiEval) Shared Task 2023". We present our approaches for task 1-A and task
2-A of the shared task which focus on persuasion technique detection and
disinformation detection respectively. Detection of persuasion techniques and
disinformation has become imperative to avoid distortion of authentic
information. The tasks use multigenre snippets of tweets and news articles for
the given binary classification problem. We experiment with several
transformer-based models that were pre-trained on the Arabic language. We
fine-tune these state-of-the-art models on the provided dataset. Ensembling is
employed to enhance the performance of the systems. We achieved a micro
F1-score of 0.742 on task 1-A (8th rank on the leaderboard) and 0.901 on task
2-A (7th rank on the leaderboard) respectively.

我们在本文中重点介绍了我们为《2023 年阿拉伯语 AI 任务评估（ArAiEval）共享任务》开展的方法。我们提出了针对共享任务的任务 1-A 和任务 2-A 的方法，重点是说服技巧检测和虚假信息检测。检测说服技巧和虚假信息已成为避免真实信息扭曲的必要手段。该任务使用多种体裁的推文和新闻文章来进行给定的二元分类问题。我们尝试了在阿拉伯语上预训练的几种基于 Transformer 的模型，并在提供的数据集上进行了微调。我们采用集成方法来提高系统的性能。我们在任务 1-A 上获得了 0.742 的微平均 F1 分数（排行榜第 8），在任务 2-A 上获得了 0.901 的微平均 F1 分数（排行榜第 7）。