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To sustain engaging conversation, it is critical for chatbots to make gooduse of relevant knowledge. Equipped with a knowledge base, chatbots are able toextract conversation-related attributes and entities to facilitate contextmodeling and response generation. In this work, we distinguish the uses ofattribute and entity and incorporate them into the encoder-decoder architecturein different manners. Based on the augmented architecture, our chatbot, namelyMike, is able to generate responses by referring to proper entities from thecollected knowledge. To validate the proposed approach, we build a movieconversation corpus on which the proposed approach significantly outperformsother four knowledge-grounded models.

Incorporating Relevant Knowledge in Context Modeling and Response Generation