  We introduce an improved variational autoencoder (VAE) for text modeling with
topic information explicitly modeled as a Dirichlet latent variable. By
providing the proposed model topic awareness, it is more superior at
reconstructing input texts. Furthermore, due to the inherent interactions
between the newly introduced Dirichlet variable and the conventional
multivariate Gaussian variable, the model is less prone to KL divergence
vanishing. We derive the variational lower bound for the new model and conduct
experiments on four different data sets. The results show that the proposed
model is superior at text reconstruction across the latent space and
classifications on learned representations have higher test accuracies.
