  Neural topic models have triggered a surge of interest in extracting topics
from text automatically since they avoid the sophisticated derivations in
conventional topic models. However, scarce neural topic models incorporate the
word relatedness information captured in word embedding into the modeling
process. To address this issue, we propose a novel topic modeling approach,
called Variational Gaussian Topic Model (VaGTM). Based on the variational
auto-encoder, the proposed VaGTM models each topic with a multivariate Gaussian
in decoder to incorporate word relatedness. Furthermore, to address the
limitation that pre-trained word embeddings of topic-associated words do not
follow a multivariate Gaussian, Variational Gaussian Topic Model with
Invertible neural Projections (VaGTM-IP) is extended from VaGTM. Three
benchmark text corpora are used in experiments to verify the effectiveness of
VaGTM and VaGTM-IP. The experimental results show that VaGTM and VaGTM-IP
outperform several competitive baselines and obtain more coherent topics.
