Recent studies try to build task-oriented dialogue systems in an end-to-end manner and the existing works make great progress on this task. However, there is still an issue need to be further considered, i.e., how to effectively represent the knowledge bases and incorporate that into dialogue systems. To solve this issue, we design a novel Transformer-based Context-aware Memory Generator to model the entities in knowledge bases, which can produce entity representations with perceiving all the relevant entities and dialogue history. Furthermore, we propose Context-aware Memory Enhanced Transformer (CMET), which can effectively aggregate information from the dialogue history and knowledge bases to generate more accurate responses. Through extensive experiments, our method can achieve superior performance over the state-of-the-art methods.
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