@inproceedings{li-etal-2021-semi,
title = "Semi-Automatic Construction of Text-to-{SQL} Data for Domain Transfer",
author = "Li, Tianyi and
Li, Sujian and
Steedman, Mark",
editor = "Oepen, Stephan and
Sagae, Kenji and
Tsarfaty, Reut and
Bouma, Gosse and
Seddah, Djam{\'e} and
Zeman, Daniel",
booktitle = "Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.iwpt-1.4",
doi = "10.18653/v1/2021.iwpt-1.4",
pages = "38--49",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Semi-Automatic Construction of Text-to-SQL Data for Domain Transfer
%A Li, Tianyi
%A Li, Sujian
%A Steedman, Mark
%Y Oepen, Stephan
%Y Sagae, Kenji
%Y Tsarfaty, Reut
%Y Bouma, Gosse
%Y Seddah, Djamé
%Y Zeman, Daniel
%S Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F li-etal-2021-semi
%X 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.
%R 10.18653/v1/2021.iwpt-1.4
%U https://aclanthology.org/2021.iwpt-1.4
%U https://doi.org/10.18653/v1/2021.iwpt-1.4
%P 38-49
Markdown (Informal)
[Semi-Automatic Construction of Text-to-SQL Data for Domain Transfer](https://aclanthology.org/2021.iwpt-1.4) (Li et al., IWPT 2021)
ACL
- Tianyi Li, Sujian Li, and Mark Steedman. 2021. Semi-Automatic Construction of Text-to-SQL Data for Domain Transfer. In Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021), pages 38–49, Online. Association for Computational Linguistics.