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  Context-aware 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.
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.
A
Transformer-based
R
Generation module
Generator
A
entities in
R
generate context-aware
produce
R
relevant entities
all the relevant entities and dialogue history
D
incorporate this module into Transformer and
A
effectively
R
better
more accurate