  End-to-End intelligent neural dialogue systems suffer from the problems of
generating inconsistent and repetitive responses. Existing dialogue models pay
attention to unilaterally incorporating personal knowledge into the dialog
while ignoring the fact that incorporating the personality-related conversation
information into personal knowledge taken as the bilateral information flow
boosts the quality of the subsequent conversation. Besides, it is indispensable
to control personal knowledge utilization over the conversation level. In this
paper, we propose a conversation-adaption multi-view persona aware response
generation model that aims at enhancing conversation consistency and
alleviating the repetition from two folds. First, we consider conversation
consistency from multiple views. From the view of the persona profile, we
design a novel interaction module that not only iteratively incorporates
personalized knowledge into each turn conversation but also captures the
personality-related information from conversation to enhance personalized
knowledge semantic representation. From the view of speaking style, we
introduce the speaking style vector and feed it into the decoder to keep the
speaking style consistency. To avoid conversation repetition, we devise a
coverage mechanism to keep track of the activation of personal knowledge
utilization. Experiments on both automatic and human evaluation verify the
superiority of our model over previous models.
