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Traditional chatbots usually need a mass of human dialogue data, especiallywhen using supervised machine learning method. Though they can easily deal withsingle-turn question answering, for multi-turn the performance is usuallyunsatisfactory. In this paper, we present Lingke, an information retrievalaugmented chatbot which is able to answer questions based on given productintroduction document and deal with multi-turn conversations. We will introducea fine-grained pipeline processing to distill responses based on unstructureddocuments, and attentive sequential context-response matching for multi-turnconversations.

Lingke: A Fine-grained Multi-turn Chatbot for Customer Service