Research on Improved Intelligent Generative Dialogue Algorithm Based on Knowledge Graph

Abstract

The rise of a new generation of artificial intelligence technology, represented by deep learning, has promoted the vigorous development of natural language processing technology. As a typical application of natural language processing technology, human-machine intelligent dialogue system, coupled with its commercial value in the fields of voice assistants and chat robots, has become a hot topic in the current academic and industrial circles. In the past, the responses generated by intelligent dialogue robots is single and universal, and even the content is inappropriate. Therefore, this study proposes to use the knowledge graph as the background knowledge when the dialogue model generates the responses[1]. In order to meet the needs of users more closely, it also proposes to introduce the user information participating in the dialogue and the dialogue scene into the model. The model is trained and evaluated on the DuRecDial public dataset, and the optimized model is compared with the original model. The experimental results show that the model with these two modules has better effect than the original model, especially in the generation index, the F1, BLEU2 and DIST-2 indexes have been improved by 0.91%, 0.5% and 0.9% respectively.