To overcome this problem and improve the quality of generated responses, we propose a novel model, named Variational Autoregressive Decoder (VAD) to iteratively incorporate a series of latent variables into the autoregressive decoder.
a distinct latent variable sampled from CVAE is associated with each time step of the generation, and it is used to condition the next state of the autoregressive decoder (e.g., the hidden state of a RNN).