  In this paper, we present the Polylingual Labeled Topic Model, a model which
combines the characteristics of the existing Polylingual Topic Model and
Labeled LDA. The model accounts for multiple languages with separate topic
distributions for each language while restricting the permitted topics of a
document to a set of predefined labels. We explore the properties of the model
in a two-language setting on a dataset from the social science domain. Our
experiments show that our model outperforms LDA and Labeled LDA in terms of
their held-out perplexity and that it produces semantically coherent topics
which are well interpretable by human subjects.
