  We present Wikipedia-based Polyglot Dirichlet Allocation (WikiPDA), a
crosslingual topic model that learns to represent Wikipedia articles written in
any language as distributions over a common set of language-independent topics.
It leverages the fact that Wikipedia articles link to each other and are mapped
to concepts in the Wikidata knowledge base, such that, when represented as bags
of links, articles are inherently language-independent. WikiPDA works in two
steps, by first densifying bags of links using matrix completion and then
training a standard monolingual topic model. A human evaluation shows that
WikiPDA produces more coherent topics than monolingual text-based LDA, thus
offering crosslinguality at no cost. We demonstrate WikiPDA's utility in two
applications: a study of topical biases in 28 Wikipedia editions, and
crosslingual supervised classification. Finally, we highlight WikiPDA's
capacity for zero-shot language transfer, where a model is reused for new
languages without any fine-tuning. Researchers can benefit from WikiPDA as a
practical tool for studying Wikipedia's content across its 299 language
editions in interpretable ways, via an easy-to-use library publicly available
at https://github.com/epfl-dlab/WikiPDA.
