  Neural conversation models such as encoder-decoder models are easy to
generate bland and generic responses. Some researchers propose to use the
conditional variational autoencoder(CVAE) which maximizes the lower bound on
the conditional log-likelihood on a continuous latent variable. With different
sampled la-tent variables, the model is expected to generate diverse responses.
Although the CVAE-based models have shown tremendous potential, their
improvement of generating high-quality responses is still unsatisfactory. In
this paper, we introduce a discrete latent variable with an explicit semantic
meaning to improve the CVAE on short-text conversation. A major advantage of
our model is that we can exploit the semantic distance between the latent
variables to maintain good diversity between the sampled latent variables.
Accordingly, we pro-pose a two-stage sampling approach to enable efficient
diverse variable selection from a large latent space assumed in the short-text
conversation task. Experimental results indicate that our model outperforms
various kinds of generation models under both automatic and human evaluations
and generates more diverse and in-formative responses.
