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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

Fri Dec 02 09:12 AM -- 09:14 AM (PST) @
Event URL: https://openreview.net/forum?id=9Ue2wCnvdMy »

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.

Author Information

Yiyun Zhao (J.P. Morgan Chase)
Jiarong Jiang (University of Utah)
Yiqun Hu (Massachusetts Institute of Technology / AWS AI Labs)
Wuwei Lan (Amazon)
Henghui Zhu (Amazon)
Anuj Chauhan (Amazon)
Hanbo Li (UC San Diego)
Lin Pan (IBM)
Jun Wang (Amazon)
Chung-Wei Hang (IBM)
Sheng Zhang (Amazon)
Mingwen Dong (AWS)
Joseph Lilien (AWS AI Labs)
Patrick Ng (Amazon)
Zhiguo Wang (, Institute of automation, Chinese academy of science)
Vittorio Castelli (Amazon)
Bing Xiang (Amazon)

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