  Controllable text generation has taken a gigantic step forward these days.
Yet existing methods are either constrained in a one-off pattern or not
efficient enough for receiving multiple conditions at every generation stage.
We propose a model-agnostic framework Plug-in Conditional Auto-Encoder for
Controllable Text Generation (PCAE) towards flexible and semi-supervised text
generation. Our framework is "plug-and-play" with partial parameters to be
fine-tuned in the pre-trained model (less than a half). Crucial to the success
of PCAE is the proposed broadcasting label fusion network for navigating the
global latent code to a specified local and confined space. Visualization of
the local latent prior well confirms the primary devotion in hidden space of
the proposed model. Moreover, extensive experiments across five related
generation tasks (from 2 conditions up to 10 conditions) on both RNN- based and
pre-trained BART [26] based auto-encoders reveal the high capability of PCAE,
which enables generation that is highly manipulable, syntactically diverse and
time-saving with minimum labeled samples. We will release our code at
https://github.com/ImKeTT/pcae.
