  With over 60M articles, Wikipedia has become the largest platform for open
and freely accessible knowledge. While it has more than 15B monthly visits, its
content is believed to be inaccessible to many readers due to the lack of
readability of its text. However, previous investigations of the readability of
Wikipedia have been restricted to English only, and there are currently no
systems supporting the automatic readability assessment of the 300+ languages
in Wikipedia. To bridge this gap, we develop a multilingual model to score the
readability of Wikipedia articles. To train and evaluate this model, we create
a novel multilingual dataset spanning 14 languages, by matching articles from
Wikipedia to simplified Wikipedia and online children encyclopedias. We show
that our model performs well in a zero-shot scenario, yielding a ranking
accuracy of more than 80% across 14 languages and improving upon previous
benchmarks. These results demonstrate the applicability of the model at scale
for languages in which there is no ground-truth data available for model
fine-tuning. Furthermore, we provide the first overview on the state of
readability in Wikipedia beyond English.
