@inproceedings{chen-etal-2023-text,
title = "Text-to-{SQL} Error Correction with Language Models of Code",
author = "Chen, Ziru and
Chen, Shijie and
White, Michael and
Mooney, Raymond and
Payani, Ali and
Srinivasa, Jayanth and
Su, Yu and
Sun, Huan",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.117",
doi = "10.18653/v1/2023.acl-short.117",
pages = "1359--1372",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Text-to-SQL Error Correction with Language Models of Code
%A Chen, Ziru
%A Chen, Shijie
%A White, Michael
%A Mooney, Raymond
%A Payani, Ali
%A Srinivasa, Jayanth
%A Su, Yu
%A Sun, Huan
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F chen-etal-2023-text
%X 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.
%R 10.18653/v1/2023.acl-short.117
%U https://aclanthology.org/2023.acl-short.117
%U https://doi.org/10.18653/v1/2023.acl-short.117
%P 1359-1372
Markdown (Informal)
[Text-to-SQL Error Correction with Language Models of Code](https://aclanthology.org/2023.acl-short.117) (Chen et al., ACL 2023)
ACL
- Ziru Chen, Shijie Chen, Michael White, Raymond Mooney, Ali Payani, Jayanth Srinivasa, Yu Su, and Huan Sun. 2023. Text-to-SQL Error Correction with Language Models of Code. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1359–1372, Toronto, Canada. Association for Computational Linguistics.