This paper investigates the optimization of propaganda technique detection in
Arabic text, including tweets \& news paragraphs, from ArAIEval shared task 1.
Our approach involves fine-tuning the AraBERT v2 model with a neural network
classifier for sequence tagging. Experimental results show relying on the first
token of the word for technique prediction produces the best performance. In
addition, incorporating genre information as a feature further enhances the
model's performance. Our system achieved a score of 25.41, placing us 4$^{th}$
on the leaderboard. Subsequent post-submission improvements further raised our
score to 26.68.

本文研究了阿拉伯文本中宣传技术检测的优化，包括推文和新闻段落，通过对阿拉伯评估任务 1 的 AraBERT v2 模型进行微调和使用神经网络分类器对序列标记进行了实验。实验结果表明，依靠单词的第一个标记进行技术预测可以取得最佳性能。此外，将体裁信息作为特征加入模型进一步增强了性能。我们的系统在排行榜上得分为 25.41，排名第四。随后的提交改进进一步提高了我们的得分至 26.68。