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International Journal of Machine Learning and Cybernetics
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17-02-2024 | Original Article

ConDA: state-based data augmentation for context-dependent text-to-SQL

Authors: Dingzirui Wang, Longxu Dou, Wanxiang Che, Jiaqi Wang, Jinbo Liu, Lixin Li, Jingan Shang, Lei Tao, Jie Zhang, Cong Fu, Xuri Song

Published in: International Journal of Machine Learning and Cybernetics | Issue 8/2024

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Abstract

The publication introduces ConDA, a state-based data augmentation method for context-dependent text-to-SQL tasks. ConDA addresses the challenge of limited training data by generating diverse and high-quality SQL-question pairs without relying on fixed templates. The method involves two steps: Generate and Filter. The Generate step creates new data by modifying SQL dialogue states, while the Filter step ensures the consistency and quality of the generated data. ConDA has been applied to two widely-used datasets, SParC and CoSQL, demonstrating significant performance improvements. The article also discusses the limitations and potential future advancements of ConDA, highlighting its potential to be extended to other context-dependent tasks and integrated with large language models.
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Metadata
Title
ConDA: state-based data augmentation for context-dependent text-to-SQL
Authors
Dingzirui Wang
Longxu Dou
Wanxiang Che
Jiaqi Wang
Jinbo Liu
Lixin Li
Jingan Shang
Lei Tao
Jie Zhang
Cong Fu
Xuri Song
Publication date
17-02-2024
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 8/2024
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-023-02086-z

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