A Knowledge-Enhanced Dialogue Model Based on Multi-Hop Information with Graph Attention

With the continuous improvement of the e-commerce ecosystem and the rapid growth of e-commerce data, inthe context of the e-commerce ecosystem, consumers ask hundreds of millions of questions every day. In order toimprove the timeliness of customer service responses, many systems have begun to use customer service robotsto respond to consumer questions, but the current customer service robots tend to respond to specific questions.For many questions that lack background knowledge, they can generate only responses that are biased towardsgenerality and repetitiveness. To better promote the understanding of dialogue and generate more meaningfulresponses, this paper introduces knowledge information into the research of question answering system by usinga knowledge graph. The unique structured knowledge base of the knowledge graph is convenient for knowledgequery, can acquire knowledge faster, and improves the background information needed for answering questions. Toavoid the lack of information in the dialogue process, this paper proposes the Multi-hop Knowledge InformationEnhanced Dialogue-Graph Attention (MKIED-GA) model. The model first retrieves the problem subgraph directlyrelated to the input information from the entire knowledge base and then uses the graph neural network as theknowledge inference module on the subgraph to encode the subgraph. The graph attention mechanism is usedto determine the one-hop and two-hop entities that are more relevant to the problem to achieve the aggregationof highly relevant neighbor information. This further enriches the semantic information to provide a betterunderstanding of the meaning of the input question and generate appropriate response information. In the processof generating a response, a multi-attention flow mechanism is used to focus on different information to promotethe generation of better responses. Experiments have proved that the model presented in this article can generatemore meaningful responses than other models.