Abstract:The existing Text2SQL methods rely heavily on the explicit mention of tables and columns in natural language queries, which causes the accuracy rate drops sharply in real-world scenarios when the same object has different names. In addition, these methods only use the database schema to capture the domain knowledge of database modeling, but the database schema, as structured metadata, has a very limited ability to express domain knowledge. This makes it difficult even for experienced programmers to fully comprehend the domain knowledge of database modeling only from the database schema, so programmers require detailed database design documents to construct SQL statements to correctly express specific queries. Therefore, we propose a Text2SQL model that uses dictionaries to expand database schema information, which parses out words or phrases in the tables and columns, queries the dictionary to obtain the semantic interpretations of these words or phrases. These semantic interpretations and the corresponding tables or columns, combined with the tables, columns and other database schema information such as primary key, foreign key are introduced to the model to learn the application field knowledge of database modeling more comprehensively. Experiments on Spider-syn and Spider dataset illustrate the effectiveness of our method, even if the table and column names used in the natural language queries are completely different from the corresponding tables and columns in the database schema, our method can get better SQL translation results, which significantly better than the latest proposed method against synonym substitution.
