  Conditional Text Generation has drawn much attention as a topic of Natural
Language Generation (NLG) which provides the possibility for humans to control
the properties of generated contents. Current conditional generation models
cannot handle emerging conditions due to their joint end-to-end learning
fashion. When a new condition added, these techniques require full retraining.
In this paper, we present a new framework named Pre-train and Plug-in
Variational Auto-Encoder (PPVAE) towards flexible conditional text generation.
PPVAE decouples the text generation module from the condition representation
module to allow "one-to-many" conditional generation. When a fresh condition
emerges, only a lightweight network needs to be trained and works as a plug-in
for PPVAE, which is efficient and desirable for real-world applications.
Extensive experiments demonstrate the superiority of PPVAE against the existing
alternatives with better conditionality and diversity but less training effort.
