  We propose a novel neural topic model in the Wasserstein autoencoders (WAE)
framework. Unlike existing variational autoencoder based models, we directly
enforce Dirichlet prior on the latent document-topic vectors. We exploit the
structure of the latent space and apply a suitable kernel in minimizing the
Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover
that MMD performs much better than the Generative Adversarial Network (GAN) in
matching high dimensional Dirichlet distribution. We further discover that
incorporating randomness in the encoder output during training leads to
significantly more coherent topics. To measure the diversity of the produced
topics, we propose a simple topic uniqueness metric. Together with the widely
used coherence measure NPMI, we offer a more wholistic evaluation of topic
quality. Experiments on several real datasets show that our model produces
significantly better topics than existing topic models.
