  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. We build the
re-ranker as a schema agnostic BERT fine-tuned classifier. We analyze relative
strengths of the text-to-SQL and re-ranker models across different query
hardness levels, and suggest how to combine the two models for optimal
performance. We demonstrate the effectiveness of the re-ranker by applying it
to two state-of-the-art text-to-SQL models, and achieve top 4 score on the
Spider leaderboard at the time of writing this article.
