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We introduce an improved variational autoencoder (VAE) for text modeling withtopic information explicitly modeled as a Dirichlet latent variable. Byproviding the proposed model topic awareness, it is more superior atreconstructing input texts. Furthermore, due to the inherent interactionsbetween the newly introduced Dirichlet variable and the conventionalmultivariate Gaussian variable, the model is less prone to KL divergencevanishing. We derive the variational lower bound for the new model and conductexperiments on four different data sets. The results show that the proposedmodel is superior at text reconstruction across the latent space andclassifications on learned representations have higher test accuracies.

Dirichlet Variational Autoencoder for Text Modeling