Text-to-SQL Error Correction with Language Models of Code

• URL: http://arxiv.org/abs/2305.13073v2
• Date: Sun, 28 May 2023 15:32:26 GMT
• Title: Text-to-SQL Error Correction with Language Models of Code
• Authors: Ziru Chen, Shijie Chen, Michael White, Raymond Mooney, Ali Payani, Jayanth Srinivasa, Yu Su, Huan Sun
• Abstract summary: In this paper, we investigate how to build automatic text-to-corpora error correction models. Noticing that token-level edits are out of context and sometimes ambiguous, we propose building clause-level edit models instead.
• Score: 24.743066730684742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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