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

Abstract

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 model, and it is verified on the open Text-to-SQL data set——Spider.Results show that the technology can effectively improve the matching accuracy of multi-table SQL statement generation, and the algorithm has an important reference value for the research of graph neural network in the text-to-SQL field.

Keywords

• text-to-sql parsing|deep learning|graph construction|graph neural network|multi-table sql statement generation