  End-to-end dialogue generation has achieved promising results without using
handcrafted features and attributes specific for each task and corpus. However,
one of the fatal drawbacks in such approaches is that they are unable to
generate informative utterances, so it limits their usage from some real-world
conversational applications. This paper attempts at generating diverse and
informative responses with a variational generation model, which contains a
joint attention mechanism conditioning on the information from both dialogue
contexts and extra knowledge.
