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Workshop: Synthetic Data for Empowering ML Research

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

Yiyun Zhao · Jiarong Jiang · Yiqun Hu · Wuwei Lan · Henghui Zhu · Anuj Chauhan · Hanbo Li · Lin Pan · Jun Wang · Chung-Wei Hang · Sheng Zhang · Mingwen Dong · Joseph Lilien · Patrick Ng · Zhiguo Wang · Vittorio Castelli · Bing Xiang


There has been increasing interest in synthesizing data to improve downstream text-to-SQL tasks. In this paper, we 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, these models have significant accuracy boosts and achieve new state-of-the-art performance on Spider.

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