@InProceedings{zhu-EtAl:2018:C18-2,
  author    = {Zhu, Pengfei  and  Zhang, Zhuosheng  and  Li, Jiangtong  and  Huang, Yafang  and  Zhao, Hai},
  title     = {Lingke: a Fine-grained Multi-turn Chatbot for Customer Service},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico},
  publisher = {Association for Computational Linguistics},
  pages     = {108--112},
  abstract  = {Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method. Though they can easily deal with single-turn question answering, for multi-turn the performance is usually unsatisfactory. In this paper, we present Lingke, an information retrieval augmented chatbot which is able to answer questions based on given product introduction document and deal with multi-turn conversations. We will introduce a fine-grained pipeline processing to distill responses based on unstructured documents, and attentive sequential context-response matching for multi-turn conversations.},
  url       = {http://www.aclweb.org/anthology/C18-2024}
}