  The Variational Autoencoder (VAE) is a popular and powerful model applied to
text modelling to generate diverse sentences. However, an issue known as
posterior collapse (or KL loss vanishing) happens when the VAE is used in text
modelling, where the approximate posterior collapses to the prior, and the
model will totally ignore the latent variables and be degraded to a plain
language model during text generation. Such an issue is particularly prevalent
when RNN-based VAE models are employed for text modelling. In this paper, we
propose a simple, generic architecture called Timestep-Wise Regularisation VAE
(TWR-VAE), which can effectively avoid posterior collapse and can be applied to
any RNN-based VAE models. The effectiveness and versatility of our model are
demonstrated in different tasks, including language modelling and dialogue
response generation.
