  Multilingual topic models enable document analysis across languages through
coherent multilingual summaries of the data. However, there is no standard and
effective metric to evaluate the quality of multilingual topics. We introduce a
new intrinsic evaluation of multilingual topic models that correlates well with
human judgments of multilingual topic coherence as well as performance in
downstream applications. Importantly, we also study evaluation for low-resource
languages. Because standard metrics fail to accurately measure topic quality
when robust external resources are unavailable, we propose an adaptation model
that improves the accuracy and reliability of these metrics in low-resource
settings.
