  Semi-Supervised Variational Autoencoders (SSVAEs) are widely used models for
data efficient learning. In this paper, we question the adequacy of the
standard design of sequence SSVAEs for the task of text classification as we
exhibit two sources of overcomplexity for which we provide simplifications.
These simplifications to SSVAEs preserve their theoretical soundness while
providing a number of practical advantages in the semi-supervised setup where
the result of training is a text classifier. These simplifications are the
removal of (i) the Kullback-Liebler divergence from its objective and (ii) the
fully unobserved latent variable from its probabilistic model. These changes
relieve users from choosing a prior for their latent variables, make the model
smaller and faster, and allow for a better flow of information into the latent
variables. We compare the simplified versions to standard SSVAEs on 4 text
classification tasks. On top of the above-mentioned simplification, experiments
show a speed-up of 26%, while keeping equivalent classification scores. The
code to reproduce our experiments is public.
