Context-aware Memory Enhanced Transformer for End-to-end Task-Oriented Dialogue Systems

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 Contextaware Memory Generation module to model the knowledge bases, which can generate context-aware entity representations with perceiving relevant entities. Furthermore, we incorporate this module into Transformer and propose Context-aware Memory Enhanced Transformer (CMET), which can aggregate information from the dialogue history and knowledge bases to generate better responses. Through extensive experiments, our method can achieve superior performance over the state-of-the-art methods.