Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker

Understand

To access data stored in relational databases, users need to understand the database schema and write a query using a query language such as SQL.

• To simplify this task, text-to-SQL models attempt to translate a user's natural language question to corresponding SQL query.
• Recently, several generative text-to-SQL models have been developed.
• We propose a novel discriminative re-ranker to improve the performance of generative text-to-SQL models by extracting the best SQL query from the beam output predicted by the text-to-SQL generator, resulting in improved performance in the cases where the best query was in the candidate list, but not at the top of the list.