  We develop the multilingual topic model for unaligned text (MuTo), a
probabilistic model of text that is designed to analyze corpora composed of
documents in two languages. From these documents, MuTo uses stochastic EM to
simultaneously discover both a matching between the languages and multilingual
latent topics. We demonstrate that MuTo is able to find shared topics on
real-world multilingual corpora, successfully pairing related documents across
languages. MuTo provides a new framework for creating multilingual topic models
without needing carefully curated parallel corpora and allows applications
built using the topic model formalism to be applied to a much wider class of
corpora.
