Despite recent progress in text-to-SQL parsing, current semantic parsers are still not accurate enough for practical use. In this paper, we investigate how to build automatic text-to-SQL error correction models. Noticing that token-level edits are out of context and sometimes ambiguous, we propose building clause-level edit models instead. Besides, while most language models of code are not specifically pre-trained for SQL, they know common data structures and their operations in programming languages such as Python. Thus, we propose a novel representation for SQL queries and their edits that adheres more closely to the pre-training corpora of language models of code. Our error correction model improves the exact set match accuracy of different parsers by 2.4-6.5 and obtains up to 4.3 point absolute improvement over two strong baselines. Our code and data are available at https://github.com/OSU-NLP-Group/Auto-SQL-Correction.
@article{arxiv.2305.13073,
title = {Text-to-SQL Error Correction with Language Models of Code},
author = {Ziru Chen and Shijie Chen and Michael White and Raymond Mooney and Ali Payani and Jayanth Srinivasa and Yu Su and Huan Sun},
journal= {arXiv preprint arXiv:2305.13073},
year = {2023}
}