Wikipedia articles (content pages) are commonly used corpora in Natural
Language Processing (NLP) research, especially in low-resource languages other
than English. Yet, a few research studies have studied the three Arabic
Wikipedia editions, Arabic Wikipedia (AR), Egyptian Arabic Wikipedia (ARZ), and
Moroccan Arabic Wikipedia (ARY), and documented issues in the Egyptian Arabic
Wikipedia edition regarding the massive automatic creation of its articles
using template-based translation from English to Arabic without human
involvement, overwhelming the Egyptian Arabic Wikipedia with articles that do
not only have low-quality content but also with articles that do not represent
the Egyptian people, their culture, and their dialect. In this paper, we aim to
mitigate the problem of template translation that occurred in the Egyptian
Arabic Wikipedia by identifying these template-translated articles and their
characteristics through exploratory analysis and building automatic detection
systems. We first explore the content of the three Arabic Wikipedia editions in
terms of density, quality, and human contributions and utilize the resulting
insights to build multivariate machine learning classifiers leveraging
articles' metadata to detect the template-translated articles automatically. We
then publicly deploy and host the best-performing classifier, XGBoost, as an
online application called EGYPTIAN WIKIPEDIA SCANNER and release the extracted,
filtered, and labeled datasets to the research community to benefit from our
datasets and the online, web-based detection system.

通过探索性分析和构建自动检测系统，本文旨在解决埃及阿拉伯维基百科中模板翻译问题，并利用文章元数据构建多变量机器学习分类器来自动检测这些模板翻译文章。最终，将最佳分类器 XGBoost 发布为名为 EGYPTIAN WIKIPEDIA SCANNER 的在线应用，并向研究界提供提取、过滤和标记的数据集，以便从我们的数据集和在线网络检测系统中获益。