Augmenting End-to-End Dialogue Systems With Commonsense Knowledge


  • Tom Young Beijing Institute of Technology
  • Erik Cambria Nanyang Technological University
  • Iti Chaturvedi Nanyang Technological University
  • Hao Zhou Tsinghua University
  • Subham Biswas Nanyang Technological University
  • Minlie Huang Tsinghua University



Commonsense Knowledge, Dialogue Systems


Building dialogue systems that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. In open-domain human-computer conversation, where the conversational agent is expected to respond to human utterances in an interesting and engaging way, commonsense knowledge has to be integrated into the model effectively. In this paper, we investigate the impact of providing commonsense knowledge about the concepts covered in the dialogue. Our model represents the first attempt to integrating a large commonsense knowledge base into end-to-end conversational models. In the retrieval-based scenario, we propose a model to jointly take into account message content and related commonsense for selecting an appropriate response. Our experiments suggest that the knowledge-augmented models are superior to their knowledge-free counterparts.




How to Cite

Young, T., Cambria, E., Chaturvedi, I., Zhou, H., Biswas, S., & Huang, M. (2018). Augmenting End-to-End Dialogue Systems With Commonsense Knowledge. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).



Main Track: NLP and Knowledge Representation