Importance of Synthesizing High-quality Data for Text-to-SQL Parsing

TL;DR

This paper highlights the importance of high-quality synthetic data in improving text-to-SQL models, proposing a novel data synthesis framework that enhances data quality and achieves state-of-the-art results.

Contribution

The paper introduces a new synthesis framework that incorporates schema relationships, strong typing, and schema-distance sampling to generate more logical and effective training data for text-to-SQL tasks.

Findings

Significant accuracy improvements on benchmarks

Achieved new state-of-the-art on Spider

Enhanced data quality leads to better model performance

Abstract

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.