Topic-Guided Variational Autoencoders for Text Generation
Table 3: Perplexity and averaged KL scores over three corpora. KL in our TGVAE denotes UKL in Eqn. (12).
['Methods', 'APNEWS PPL', 'APNEWS KL', 'IMDB PPL', 'IMDB KL', 'BNC PPL', 'BNC KL']
Results In comparison, our TGVAE achieves a lower perplexity upper bound, with a relative larger UKL. We attribute the improvements to our topic guided GMM model design, which provides additional topical clustering information in the latent space; the Householder flow also boosts the posterior inference for our TGVAE. We also observe consistent improvements with the number of topics, which demonstrates the efficiency of our TGVAE.