History-Adaption Knowledge Incorporation Mechanism for Multi-Turn Dialogue System


  • Yajing Sun University of Chinese Academy of Sciences
  • Yue Hu University of Chinese Academy of Sciences
  • Luxi Xing Chinese Academy of Sciences
  • Jing Yu Chinese Academy of Sciences
  • Yuqiang Xie Chinese Academy of Sciences




Keeping the conversation consistent and avoiding its repetition are two key factors to construct an intelligent multi-turn knowledge-grounded dialogue system. Although some works tend to combine history with external knowledge such as personal background information to boost dialogue quality, they are prone to ignore the fact that incorporating the same knowledge multiple times into the conversation leads to repetition. The main reason is the lack of effective control over the use of knowledge on the conversation level. So we design a history-adaption knowledge incorporation mechanism to build an effective multi-turn dialogue model. Our proposed model addresses repetition by recurrently updating the knowledge from the conversation level and progressively incorporating it into the history step-by-step. And the knowledge-grounded history representation also enhances the conversation consistency. Experimental results show that our proposed model significantly outperforms several retrieval-based models on some benchmark datasets. The human evaluation demonstrates that our model can maintain conversation consistent and reduce conversation repetition.




How to Cite

Sun, Y., Hu, Y., Xing, L., Yu, J., & Xie, Y. (2020). History-Adaption Knowledge Incorporation Mechanism for Multi-Turn Dialogue System. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8944-8951. https://doi.org/10.1609/aaai.v34i05.6425



AAAI Technical Track: Natural Language Processing