Learning Personalized End-to-End Task-Oriented Dialogue Generation

Building personalized task-oriented dialogue system is an important but challenging task. Significant success has been achieved by selecting the responses from the pre-defined template. However, preparing massive response template is time-consuming and human-labor intensive. In this paper, we propose an end-to-end framework based on the memory networks for responses generation in the personalized task-oriented dialog system. The static attention mechanism is used to encode the user-conversation relationship to form a global vector representation, and the dynamic attention mechanism is used to obtain import local information during the decoding phase. In addition, we propose a gating mechanism to incorporate user information into the network to enhance the personalized ability of the response. Experiments on the benchmark dataset show that our model achieves better performance than the strong baseline methods in personalized task-oriented dialogue generation.