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

• Published in: Computer modeling in engineering & sciences Vol. 128; no. 2; pp. 403 - 426
• Main Authors: Bi, Zhongqin, Wang, Shiyang, Chen, Yan, Li, Yongbin, Yoon Kim, Jung
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 01.01.2021
• Subjects: Continuous improvement; Conversation Generation; Customer services; E-Commerce Ecosystem; Electronic commerce; Graph Attention; Graph Neural Network; Graph neural networks; Graph theory; Knowledge; Knowledge bases (artificial intelligence); Knowledge Graph; Knowledge representation; Questions; Service robots
• ISSN: 1526-1492; 1526-1506; 1526-1506
• DOI: 10.32604/cmes.2021.016729

With the continuous improvement of the e-commerce ecosystem and the rapid growth of e-commerce data, in the context of the e-commerce ecosystem, consumers ask hundreds of millions of questions every day. In order to improve the timeliness of customer service responses, many systems have begun to use customer service robots to 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 towards generality and repetitiveness. To better promote the understanding of dialogue and generate more meaningful responses, this paper introduces knowledge information into the research of question answering system by using a knowledge graph. The unique structured knowledge base of the knowledge graph is convenient for knowledge query, can acquire knowledge faster, and improves the background information needed for answering questions. To avoid the lack of information in the dialogue process, this paper proposes the Multi-hop Knowledge Information Enhanced Dialogue-Graph Attention (MKIED-GA) model. The model first retrieves the problem subgraph directly related to the input information from the entire knowledge base and then uses the graph neural network as the knowledge inference module on the subgraph to encode the subgraph. The graph attention mechanism is used to determine the one-hop and two-hop entities that are more relevant to the problem to achieve the aggregation of highly relevant neighbor information. This further enriches the semantic information to provide a better understanding of the meaning of the input question and generate appropriate response information. In the process of generating a response, a multi-attention flow mechanism is used to focus on different information to promote the generation of better responses. Experiments have proved that the model presented in this article can generate more meaningful responses than other models.