An Overview on Controllable Text Generation via Variational Auto-Encoders

Understand

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.