Knowledge Graph Grounded Goal Planning for Open-Domain Conversation Generation


  • Jun Xu Harbin Institute of Technology
  • Haifeng Wang Baidu
  • Zhengyu Niu Baidu
  • Hua Wu Baidu
  • Wanxiang Che Harbin Institute of Technology



Previous neural models on open-domain conversation generation have no effective mechanisms to manage chatting topics, and tend to produce less coherent dialogs. Inspired by the strategies in human-human dialogs, we divide the task of multi-turn open-domain conversation generation into two sub-tasks: explicit goal (chatting about a topic) sequence planning and goal completion by topic elaboration. To this end, we propose a three-layer Knowledge aware Hierarchical Reinforcement Learning based Model (KnowHRL). Specifically, for the first sub-task, the upper-layer policy learns to traverse a knowledge graph (KG) in order to plan a high-level goal sequence towards a good balance between dialog coherence and topic consistency with user interests. For the second sub-task, the middle-layer policy and the lower-layer one work together to produce an in-depth multi-turn conversation about a single topic with a goal-driven generation mechanism. The capability of goal-sequence planning enables chatbots to conduct proactive open-domain conversations towards recommended topics, which has many practical applications. Experiments demonstrate that our model outperforms state of the art baselines in terms of user-interest consistency, dialog coherence, and knowledge accuracy.




How to Cite

Xu, J., Wang, H., Niu, Z., Wu, H., & Che, W. (2020). Knowledge Graph Grounded Goal Planning for Open-Domain Conversation Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 9338-9345.



AAAI Technical Track: Natural Language Processing