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arXiv · 2210.16613

Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers

Abhijeet Awasthi·Ashutosh Sathe·Sunita Sarawagi
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Abstract

Text-to-SQL parsers typically struggle with databases unseen during the train time. Adapting parsers to new databases is a challenging problem due to the lack of natural language queries in the new schemas. We present ReFill, a framework for synthesizing high-quality and textually diverse parallel datasets for adapting a Text-to-SQL parser to a target schema. ReFill learns to retrieve-and-edit text queries from the existing schemas and transfers them to the target schema. We show that retrieving diverse existing text, masking their schema-specific tokens, and refilling with tokens relevant to the target schema, leads to significantly more diverse text queries than achievable by standard SQL-to-Text generation methods. Through experiments spanning multiple databases, we demonstrate that fine-tuning parsers on datasets synthesized using ReFill consistently outperforms the prior data-augmentation methods.

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Abhijeet Awasthi, Ashutosh Sathe, Sunita Sarawagi. 2022-10-29. Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers. https://arxiv.org/abs/2210.16613

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