  Leveraging persona information of users in Neural Response Generators (NRG)
to perform personalized conversations has been considered as an attractive and
important topic in the research of conversational agents over the past few
years. Despite of the promising progresses achieved by recent studies in this
field, persona information tends to be incorporated into neural networks in the
form of user embeddings, with the expectation that the persona can be involved
via the End-to-End learning. This paper proposes to adopt the
personality-related characteristics of human conversations into variational
response generators, by designing a specific conditional variational
autoencoder based deep model with two new regularization terms employed to the
loss function, so as to guide the optimization towards the direction of
generating both persona-aware and relevant responses. Besides, to reasonably
evaluate the performances of various persona modeling approaches, this paper
further presents three direct persona-oriented metrics from different
perspectives. The experimental results have shown that our proposed methodology
can notably improve the performance of persona-aware response generation, and
the metrics are reasonable to evaluate the results.
