  Deep generative models have been widely used in several areas of NLP, and
various techniques have been proposed to augment them or address their training
challenges. In this paper, we propose a simple modification to Variational
Autoencoders (VAEs) by using an Isotropic Gaussian Posterior (IGP) that allows
for better utilisation of their latent representation space. This model avoids
the sub-optimal behavior of VAEs related to inactive dimensions in the
representation space. We provide both theoretical analysis, and empirical
evidence on various datasets and tasks that show IGP leads to consistent
improvement on several quantitative and qualitative grounds, from downstream
task performance and sample efficiency to robustness. Additionally, we give
insights about the representational properties encouraged by IGP and also show
that its gain generalises to image domain as well.

Machine Learning;Computation and Language