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Two types of knowledge, triples from knowledge graphs and texts fromdocuments, have been studied for knowledge aware open-domain conversationgeneration, in which graph paths can narrow down vertex candidates forknowledge selection decision, and texts can provide rich information forresponse generation. Fusion of a knowledge graph and texts might yield mutuallyreinforcing advantages, but there is less study on that. To address thischallenge, we propose a knowledge aware chatting machine with three components,an augmented knowledge graph with both triples and texts, knowledge selector,and knowledge aware response generator. For knowledge selection on the graph,we formulate it as a problem of multi-hop graph reasoning to effectivelycapture conversation flow, which is more explainable and flexible in comparisonwith previous work. To fully leverage long text information that differentiatesour graph from others, we improve a state of the art reasoning algorithm withmachine reading comprehension technology. We demonstrate the effectiveness ofour system on two datasets in comparison with state-of-the-art models.

Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs