Survey of Research on Knowledge-Driven Dialogue Generation Models

Abstract

Knowledge-driven dialogue generation models aim to enhance dialogue generation models by using different forms of knowledge, so that dialogue generation models can not only learn semantic interactions from dialogue data, but also deeply understand user input, background knowledge and dialogue context to generate more reasonable, diverse, informative and anthropomorphic responses, and thus promote the development of dialogue systems. Currently, the related work is still in the early stages of exploration, and there is a lack of comprehensive reviews and systematic summaries of existing results. This paper provides a comprehensive review of the research on knowledge-driven dialogue generation models. Firstly, in response to the existing research results, it sorts out and introduces the current knowledge-driven dialogue generation tasks and the main problems encountered, and provides detailed task definitions and problem definitions. Secondly, it organizes and introduces the datasets required for the modeling of knowledge-driven dialogue generation models. Then, it focuses on the improvement, research status, evaluation indicators involved, and performance of each model in the process of knowledge-driven dialogue generation research, including knowledge acquisition, knowledge representation, knowledge selection, and knowledge integration-related studies. Finally, the future development directions of knowledge-based dialogue generation models are prospected.

Keywords

• dialogue generation model; dialogue systems; external knowledge; knowledge-driven dialogue generation