Importance of Synthesizing High-quality Data for Text-to-SQL Parsing
This work addresses a critical bottleneck in text-to-SQL parsing by improving data synthesis, which is important for developers and researchers in natural language processing and database interfaces, though it is incremental as it builds on existing methods.
The paper tackled the problem of low-quality synthetic data hindering text-to-SQL parsing performance by proposing a novel synthesis framework that incorporates schema relationships and strong typing, resulting in significant accuracy boosts and new state-of-the-art performance on the Spider benchmark.
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.