@inproceedings{hu-etal-2023-importance,
    title = "Importance of Synthesizing High-quality Data for Text-to-{SQL} Parsing",
    author = "Hu, Yiqun  and
      Zhao, Yiyun  and
      Jiang, Jiarong  and
      Lan, Wuwei  and
      Zhu, Henghui  and
      Chauhan, Anuj  and
      Li, Alexander Hanbo  and
      Pan, Lin  and
      Wang, Jun  and
      Hang, Chung-Wei  and
      Zhang, Sheng  and
      Guo, Jiang  and
      Dong, Mingwen  and
      Lilien, Joseph  and
      Ng, Patrick  and
      Wang, Zhiguo  and
      Castelli, Vittorio  and
      Xiang, Bing",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.86",
    doi = "10.18653/v1/2023.findings-acl.86",
    pages = "1327--1343",
    abstract = "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 three shortcomings: illogical synthetic SQL queries from independent column sampling, arbitrary table joins, and language gaps between the synthesized SQL and natural language question (NLQ) pair. To address these issues, we propose a novel synthesis framework that imposes strong typing constraints, incorporates key relationships from schema, 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 NLQ. When existing powerful text-to-SQL parsers are pretrained on our high-quality synthesized data, these models have significant accuracy boosts and achieve new state-of-the-art performance on Spider. We also demonstrate the effectiveness of our techniques with ablation studies",
}

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 three shortcomings: illogical synthetic SQL queries from independent column sampling, arbitrary table joins, and language gaps between the synthesized SQL and natural language question (NLQ) pair. To address these issues, we propose a novel synthesis framework that imposes strong typing constraints, incorporates key relationships from schema, 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 NLQ. When existing powerful text-to-SQL parsers are pretrained on our high-quality synthesized data, these models have significant accuracy boosts and achieve new state-of-the-art performance on Spider. We also demonstrate the effectiveness of our techniques with ablation studies
Hu, Yiqun Zhao, Yiyun Jiang, Jiarong Lan, Wuwei Zhu, Henghui Chauhan, Anuj Li, Alex er Hanbo Pan, Lin Wang, Jun Hang, Chung-Wei Zhang, Sheng Guo, Jiang Dong, Mingwen Lilien, Joseph Ng, Patrick Wang, Zhiguo Castelli, Vittorio Xiang, Bing
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing