  We propose a new problem called coordinated topic modeling that imitates
human behavior while describing a text corpus. It considers a set of
well-defined topics like the axes of a semantic space with a reference
representation. It then uses the axes to model a corpus for easily
understandable representation. This new task helps represent a corpus more
interpretably by reusing existing knowledge and benefits the corpora comparison
task. We design ECTM, an embedding-based coordinated topic model that
effectively uses the reference representation to capture the target
corpus-specific aspects while maintaining each topic's global semantics. In
ECTM, we introduce the topic- and document-level supervision with a
self-training mechanism to solve the problem. Finally, extensive experiments on
multiple domains show the superiority of our model over other baselines.
