IMDP: A Unify Dialogue Framework with Awareness and Understanding for Implicit Personalized Dialogue Generation

Personalized chatbots concentrate on learning human personalities, making them act similar to real users. When it is authorized to respond to other people’s messages, it has the same way of speaking as the user. Many personalized methods have been proposed to use several persona descriptions or key-value-based persona information to assign a personality for dialogue chatbots. Most of them employ explicit user profiles. However, obtaining generous explicit user profiles are extremely time-consuming and requires tremendous manual labor. In addition, explicit user profiles cannot be updated as the user’s interests change. In this article, we propose a generation-based personalized chatbot model, IMDPchat, that learns latent user representation from the abundant users’ dialogue history. Specially, we train a personalized language model to build a global user profile using dialogue responses. To take full advantage of users’ information used in the historical dialogue, we establish a key-value memory network and construct a post-sensitive personalized selection module. The above two parts is context-aware: we endow higher weights to historical post-response pairs that are connected to the current post. To predict more personalized responses, we design a personalized response decoder that can well integrate two decoding modes, including generating tokens and copying personalized words. Experimental results indicate that the IMDPchat model outperforms previous baselines remarkably.