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.

翻译：终端到终端智能神经对话系统存在产生不一致和重复反应的问题。现有的对话模式关注将个人知识单方面纳入对话,而忽视以下事实:在双边信息流动提高后续对话的质量时,将个性相关对话信息纳入个人知识,因为双边信息流动提高了后续对话的质量;此外,在对话层面控制个人知识的利用是不可或缺的。在本文件中,我们提议了一个对话-适应多视图人意识反应生成模型,目的是加强对话的一致性,减少两个折叠的重复。首先,我们考虑从多个角度对对话的一致性。从个人特征的角度来看,我们设计了一个新的互动模块,不仅将个性化知识反复纳入每次对话,而且从对话中捕捉个性相关信息,以加强个性化知识的语义表达方式。从发言风格的观点来看,我们引入语音风格矢量并将其输入到解密器中,以保持语音风格的一致性。为了避免重复对话,我们设计了一个覆盖机制,以跟踪个人知识的启用情况。关于自动和人性评估的实验可以验证我们模式相对于以往模式的优越性。