@inproceedings{awasthi-etal-2022-diverse,
    title = "Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-{SQL} Parsers",
    author = "Awasthi, Abhijeet  and
      Sathe, Ashutosh  and
      Sarawagi, Sunita",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.emnlp-main.794/",
    doi = "10.18653/v1/2022.emnlp-main.794",
    pages = "11548--11562",
    abstract = "Text-to-SQL parsers typically struggle with databases unseen during the train time. Adapting Text-to-SQL parsers to new database schemas is a challenging problem owing to a vast diversity of schemas and zero availability of natural language queries in new schemas. We present ReFill, a framework for synthesizing high-quality and textually diverse parallel datasets for adapting Text-to-SQL parsers. Unlike prior methods that utilize SQL-to-Text generation, ReFill learns to retrieve-and-edit text queries in existing schemas and transfer them to the new schema. ReFill utilizes a simple method for retrieving diverse existing text, masking their schema-specific tokens, and refilling with tokens relevant to the new schema. We show that this process leads to significantly more diverse text queries than achievable by standard SQL-to-Text generation models. Through experiments on several databases, we show that adapting a parser by finetuning it on datasets synthesized by ReFill consistently outperforms prior data-augmentation methods."
}