  Topic models are evaluated based on their ability to describe documents well
(i.e. low perplexity) and to produce topics that carry coherent semantic
meaning. In topic modeling so far, perplexity is a direct optimization target.
However, topic coherence, owing to its challenging computation, is not
optimized for and is only evaluated after training. In this work, under a
neural variational inference framework, we propose methods to incorporate a
topic coherence objective into the training process. We demonstrate that such a
coherence-aware topic model exhibits a similar level of perplexity as baseline
models but achieves substantially higher topic coherence.

Computation and Language;Machine Learning