基于双层记忆网络的多领域 端到端任务型对话系统.

To effectively incorporate knowledge into dialogue reasoning, this paper proposed a multi-domain end-to-end task-oriented dialogue system based on a double-layer memory network. Firstly, it used the form of knowledge rows instead of triples to represent knowledge, which improved the performance of knowledge location. Secondly, it used a double-layer memory network structure to model knowledge and dialogue history separately, which improved the reasoning ability of the model. Finally, it used a dynamic encoder to encode data in various fields to improve the generalization ability of the model. Through experimental analysis, the F1 and BLEU indicators of the model are improved through compared with the comparison algorithm on the InCar and CamRest datasets, which verifies the effectiveness and advancement of the model. [ABSTRACT FROM AUTHOR]