Leveraging Corpus Metadata to Detect Template-based Translation: An
  Exploratory Case Study of the Egyptian Arabic Wikipedia Edition
Saied Alshahrani Hesham Haroon Ali Elfilali Mariama Njie Jeanna Matthews
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.