Technical Research of Graph Neural Network for Text-to-SQL Parsing

• Published in: Ji suan ji ke xue Vol. 49; no. 4; pp. 110 - 115
• Main Author: CAO He-xin, ZHAO Liang, LI Xue-feng
• Format: Journal Article
• Language: Chinese
• Published: Editorial office of Computer Science 01.04.2022
• Subjects: text-to-sql parsing|deep learning|graph construction|graph neural network|multi-table sql statement generation
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.210200173

The Text-to-SQL task in the field of semantic parsing is of great significance for realizing database-based automatic question and answer.At present, deep learning models, such as sequence generation model Seq2Seq, has achieved significant effects in single-table SQL queries.However, the problem of multi-table SQL queries remains to be solved.Graph neural network can effectively extract the associated information between databases, tables and questions, enrich the semantic information in the parsing process, and improve the accuracy of multi-table SQL queries.This paper proposes an adaptive graph construction method and graph encoding method.Question information is introduced into the existing Text-to-SQL model, and the graph network initialized weights are generated by convolution operation on the splicing word vector of the question sentence and the database.General training can be achieved for different databases of the same type.The IRNet framework and relational expansion are used to design the overall m