  Recent advances in neural-based generative modeling have reignited the hopes
of having computer systems capable of conversing with humans and able to
understand natural language. The employment of deep neural architectures has
been largely explored in a multitude of context and tasks to fulfill various
user needs. On one hand, producing textual content that meets specific
requirements is of priority for a model to seamlessly conduct conversations
with different groups of people. On the other hand, latent variable models
(LVM) such as variational auto-encoders (VAEs) as one of the most popular
genres of generative models are designed to characterize the distributional
pattern of textual data. Thus they are inherently capable of learning the
integral textual features that are worth exploring for controllable pursuits.
  \noindent This overview gives an introduction to existing generation schemes,
problems associated with text variational auto-encoders, and a review of
several applications about the controllable generation that are instantiations
of these general formulations,\footnote{A detailed paper list is available at
\url{https://github.com/ImKeTT/CTG-latentAEs}} as well as related datasets,
metrics and discussions for future researches. Hopefully, this overview will
provide an overview of living questions, popular methodologies and raw thoughts
for controllable language generation under the scope of variational
auto-encoder.

Computation and Language