  In human conversations, due to their personalities in mind, people can easily
carry out and maintain the conversations. Giving conversational context with
persona information to a chatbot, how to exploit the information to generate
diverse and sustainable conversations is still a non-trivial task. Previous
work on persona-based conversational models successfully make use of predefined
persona information and have shown great promise in delivering more realistic
responses. And they all learn with the assumption that given a source input,
there is only one target response. However, in human conversations, there are
massive appropriate responses to a given input message. In this paper, we
propose a memory-augmented architecture to exploit persona information from
context and incorporate a conditional variational autoencoder model together to
generate diverse and sustainable conversations. We evaluate the proposed model
on a benchmark persona-chat dataset. Both automatic and human evaluations show
that our model can deliver more diverse and more engaging persona-based
responses than baseline approaches.
