Year: 2023
Type: preprint
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.
Source: arXiv (Cornell University)
Institutions: The Ohio State University, The University of Texas at Austin
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: CAREER: Towards Interactive and Transparent Question Answering with Applications in the Clinical Domain, AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE), III: Small: Towards Resolving Ad-hoc Concept Queries with Table Answers via Multi-source Data Mining, G6671297155
OpenAlex ID: W4377865157