  Scientific publications have evolved several features for mitigating
vocabulary mismatch when indexing, retrieving, and computing similarity between
articles. These mitigation strategies range from simply focusing on high-value
article sections, such as titles and abstracts, to assigning keywords, often
from controlled vocabularies, either manually or through automatic annotation.
Various document representation schemes possess different cost-benefit
tradeoffs. In this paper, we propose to model different representations of the
same article as translations of each other, all generated from a common latent
representation in a multilingual topic model. We start with a methodological
overview on latent variable models for parallel document representations that
could be used across many information science tasks. We then show how solving
the inference problem of mapping diverse representations into a shared topic
space allows us to evaluate representations based on how topically similar they
are to the original article. In addition, our proposed approach provides means
to discover where different concept vocabularies require improvement.
