An Exploratory Study on Model Compression for Text-to-SQL

Shuo Sun; Yuze Gao; Yuchen Zhang; Jian Su; Bin Chen; Yingzhan Lin; Shuqi Sun

doi:10.18653/v1/2023.findings-acl.740

An Exploratory Study on Model Compression for Text-to-SQL

Shuo Sun, Yuze Gao, Yuchen Zhang, Jian Su, Bin Chen, Yingzhan Lin, Shuqi Sun

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Abstract

Text-to-SQL translates user queries into SQL statements that can retrieve relevant answers from relational databases. Recent approaches to Text-to-SQL rely on pre-trained language models that are computationally expensive and technically challenging to deploy in real-world applications that require real-time or on-device processing capabilities. In this paper, we perform a focused study on the feasibility of applying recent model compression techniques to sketch-based and sequence-to-sequence Text-to-SQL models. Our results reveal that sketch-based Text-to-SQL models generally have higher inference efficiency and respond better to model compression than sequence-to-sequence models, making them ideal for real-world deployments, especially in use cases with simple SQL statements.

Anthology ID:: 2023.findings-acl.740
Volume:: Findings of the Association for Computational Linguistics: ACL 2023
Month:: July
Year:: 2023
Address:: Toronto, Canada
Editors:: Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:: Findings
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 11647–11654
Language:
URL:: https://aclanthology.org/2023.findings-acl.740/
DOI:: 10.18653/v1/2023.findings-acl.740
Bibkey:
Cite (ACL):: Shuo Sun, Yuze Gao, Yuchen Zhang, Jian Su, Bin Chen, Yingzhan Lin, and Shuqi Sun. 2023. An Exploratory Study on Model Compression for Text-to-SQL. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11647–11654, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):: An Exploratory Study on Model Compression for Text-to-SQL (Sun et al., Findings 2023)
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PDF:: https://aclanthology.org/2023.findings-acl.740.pdf

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@inproceedings{sun-etal-2023-exploratory,
    title = "An Exploratory Study on Model Compression for Text-to-{SQL}",
    author = "Sun, Shuo  and
      Gao, Yuze  and
      Zhang, Yuchen  and
      Su, Jian  and
      Chen, Bin  and
      Lin, Yingzhan  and
      Sun, Shuqi",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.740/",
    doi = "10.18653/v1/2023.findings-acl.740",
    pages = "11647--11654",
    abstract = "Text-to-SQL translates user queries into SQL statements that can retrieve relevant answers from relational databases. Recent approaches to Text-to-SQL rely on pre-trained language models that are computationally expensive and technically challenging to deploy in real-world applications that require real-time or on-device processing capabilities. In this paper, we perform a focused study on the feasibility of applying recent model compression techniques to sketch-based and sequence-to-sequence Text-to-SQL models. Our results reveal that sketch-based Text-to-SQL models generally have higher inference efficiency and respond better to model compression than sequence-to-sequence models, making them ideal for real-world deployments, especially in use cases with simple SQL statements."
}

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%0 Conference Proceedings
%T An Exploratory Study on Model Compression for Text-to-SQL
%A Sun, Shuo
%A Gao, Yuze
%A Zhang, Yuchen
%A Su, Jian
%A Chen, Bin
%A Lin, Yingzhan
%A Sun, Shuqi
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sun-etal-2023-exploratory
%X Text-to-SQL translates user queries into SQL statements that can retrieve relevant answers from relational databases. Recent approaches to Text-to-SQL rely on pre-trained language models that are computationally expensive and technically challenging to deploy in real-world applications that require real-time or on-device processing capabilities. In this paper, we perform a focused study on the feasibility of applying recent model compression techniques to sketch-based and sequence-to-sequence Text-to-SQL models. Our results reveal that sketch-based Text-to-SQL models generally have higher inference efficiency and respond better to model compression than sequence-to-sequence models, making them ideal for real-world deployments, especially in use cases with simple SQL statements.
%R 10.18653/v1/2023.findings-acl.740
%U https://aclanthology.org/2023.findings-acl.740/
%U https://doi.org/10.18653/v1/2023.findings-acl.740
%P 11647-11654

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Markdown (Informal)

[An Exploratory Study on Model Compression for Text-to-SQL](https://aclanthology.org/2023.findings-acl.740/) (Sun et al., Findings 2023)

An Exploratory Study on Model Compression for Text-to-SQL (Sun et al., Findings 2023)

ACL

Shuo Sun, Yuze Gao, Yuchen Zhang, Jian Su, Bin Chen, Yingzhan Lin, and Shuqi Sun. 2023. An Exploratory Study on Model Compression for Text-to-SQL. In Findings of the Association for Computational Linguistics: ACL 2023, pages 11647–11654, Toronto, Canada. Association for Computational Linguistics.