Knowledge Aware Conversation Generation with Explainable Reasoning on Augmented Graphs.

Two types of knowledge, triples from knowledge graphs and texts from unstructured documents, have been studied for knowledge aware open-domain conversation generation, in which triple attributes or graph paths can narrow down vertex candidates for knowledge selection decision, and texts can provide rich information for response generation. Fusion of a knowledge graph and texts might yield mutually reinforcing advantages for conversation generation, but there is less study on that. To address this challenge, we propose a knowledge aware chatting machine with three components, an augmented knowledge graph containing both triples and texts, knowledge selector, and response generator. For knowledge selection on the graph, we formulate it as a problem of multi-hop graph reasoning that is more explainable and flexible in comparison with previous works. To fully leverage long text information that differentiates our graph from others, we improve a state of the art reasoning algorithm with machine reading comprehension technology. We demonstrate that supported by such unified knowledge and explainable knowledge selection method, our system can generate more appropriate and informative responses than baselines.