Semi-Automatic Construction of Text-to-SQL Data for Domain Transfer

Tianyi Li, Sujian Li, Mark Steedman


Abstract
Strong and affordable in-domain data is a desirable asset when transferring trained semantic parsers to novel domains. As previous methods for semi-automatically constructing such data cannot handle the complexity of realistic SQL queries, we propose to construct SQL queries via context-dependent sampling, and introduce the concept of topic. Along with our SQL query construction method, we propose a novel pipeline of semi-automatic Text-to-SQL dataset construction that covers the broad space of SQL queries. We show that the created dataset is comparable with expert annotation along multiple dimensions, and is capable of improving domain transfer performance for SOTA semantic parsers.
Anthology ID:
2021.iwpt-1.4
Volume:
Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | IWPT
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
38–49
Language:
URL:
https://aclanthology.org/2021.iwpt-1.4
DOI:
10.18653/v1/2021.iwpt-1.4
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.iwpt-1.4.pdf