  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.
