Piecewise Latent Variables for Neural Variational Text Processing
Piecewise Latent Variables for Neural Variational Text Processing
Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variables, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic …