Coherence-Aware Neural Topic Modeling
Ran Ding, Ramesh M. Nallapati, Bing Xiang · 2018
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 coherenceaware topic model exhibits a similar level of perplexity as baseline models but achieves substantially higher topic coherence.