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.

翻译：本文阐述了我们在"2023年阿拉伯语人工智能任务评测（ArAiEval）共享任务"中的方法体系。我们分别针对任务1-A（说服技术检测）和任务2-A（虚假信息检测）提出了解决方案。为避免真实信息被扭曲，检测说服技术与虚假信息已势在必行。这两个任务属于二元分类问题，输入数据为多体裁推文和新闻片段。我们实验了多种基于阿拉伯语预训练的Transformer模型，并在给定数据集上对这些先进模型进行微调。通过集成学习策略提升系统性能，最终在任务1-A中取得0.742的微平均F1值（排行榜第8名），在任务2-A中取得0.901的微平均F1值（排行榜第7名）。