@inproceedings{liu-etal-2018-knowledge,
    title = "Knowledge Diffusion for Neural Dialogue Generation",
    author = "Liu, Shuman  and
      Chen, Hongshen  and
      Ren, Zhaochun  and
      Feng, Yang  and
      Liu, Qun  and
      Yin, Dawei",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1138",
    doi = "10.18653/v1/P18-1138",
    pages = "1489--1498",
    abstract = "End-to-end neural dialogue generation has shown promising results recently, but it does not employ knowledge to guide the generation and hence tends to generate short, general, and meaningless responses. In this paper, we propose a neural knowledge diffusion (NKD) model to introduce knowledge into dialogue generation. This method can not only match the relevant facts for the input utterance but diffuse them to similar entities. With the help of facts matching and entity diffusion, the neural dialogue generation is augmented with the ability of convergent and divergent thinking over the knowledge base. Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats. The experiment results also show that our model outperforms competitive baseline models significantly.",
}