Diverse dialogue generation by fusing mutual persona-aware and self-transferrer

In recent years, personalized dialogue generation has attracted researchers' attention due to its wide range of applications. Although there has been much excellent research, current chatbots are known to generate uninteresting responses without personality. In a general way, persona information is adopted to mitigate these issues. Yet, how to integrate persona information reasonably based on historical dialogue in responses is still a severe challenge. Thus, we present a solution that consolidates the persona information memory process and pretrained method to generate personalized and fluency responses. Moreover, we adopt a reinforcement learning algorithm to joint each part of the dialogue model. Last but not least, we present a self-learning framework to explore the hypothetical space, and make the responses more personalized and fascinating. Extensive experiments on the large-scale dialogue public dataset ConvAI2 verify the effectiveness of our method.