  Recently, there has been increasing interest in synthesizing data to improve
downstream text-to-SQL tasks. In this paper, we first examined the existing
synthesized datasets and discovered that state-of-the-art text-to-SQL
algorithms did not further improve on popular benchmarks when trained with
augmented synthetic data. We observed two shortcomings: illogical synthetic SQL
queries from independent column sampling and arbitrary table joins. To address
these issues, we propose a novel synthesis framework that incorporates key
relationships from schema, imposes strong typing, and conducts
schema-distance-weighted column sampling. We also adopt an intermediate
representation (IR) for the SQL-to-text task to further improve the quality of
the generated natural language questions. When existing powerful semantic
parsers are pre-finetuned on our high-quality synthesized data, our experiments
show that these models have significant accuracy boosts on popular benchmarks,
including new state-of-the-art performance on Spider.
