  We present a dialogue generation model that directly captures the variability
in possible responses to a given input, which reduces the `boring output' issue
of deterministic dialogue models. Experiments show that our model generates
more diverse outputs than baseline models, and also generates more consistently
acceptable output than sampling from a deterministic encoder-decoder model.
