Graph-Structured Context Understanding for Knowledge-grounded Response Generation

In this work, we establish a context graph from both conversation utterances and external knowledge, and develop a novel graph-based encoder to better understand the conversation context. Specifically, the encoder fuses the information in the context graph stage-by-stage and provides global context-graph-aware representations of each node in the graph to facilitate knowledge-grounded response generation. On a large-scale conversation corpus, we validate the effectiveness of the proposed approach and demonstrate the benefit of knowledge in conversation understanding.