ConDA: state-based data augmentation for context-dependent text-to-SQL
The context-dependent text-to-SQL task has profound real-world implications, as it facilitates users in extracting knowledge from vast databases, which allows users to acquire the information interactively for better accuracy. Unfortunately, current models struggle to address this task effectively d...
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Published in | International journal of machine learning and cybernetics Vol. 15; no. 8; pp. 3157 - 3168 |
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Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.08.2024
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | The context-dependent text-to-SQL task has profound real-world implications, as it facilitates users in extracting knowledge from vast databases, which allows users to acquire the information interactively for better accuracy. Unfortunately, current models struggle to address this task effectively due to the scarcity of data led by the high annotation overhead. The most straightforward method for addressing this problem is data augmentation, which aims at scaling up the parsing corpus. However, the naive methods suffer from the low diversity of the augmented data. To address this limitation, we propose the state-based
CON
text-dependent text-to-SQL
D
ata
A
ugmentation (
ConDA
), which generate and filter augmented data based on the dialogue state, which has higher diversity. Experimental results show that
ConDA
yields performance improvement on all experimental datasets with an average boosting of
1.6
%
, proving the effectiveness of our method. |
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ISSN: | 1868-8071 1868-808X |
DOI: | 10.1007/s13042-023-02086-z |