BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domain

Rahul Kumar, Amar Raja Dibbu, Shrutendra Harsola, Vignesh Subrahmaniam, Ashutosh Modi


Abstract
Several large-scale datasets (e.g., WikiSQL, Spider) for developing natural language interfaces to databases have recently been proposed. These datasets cover a wide breadth of domains but fall short on some essential domains, such as finance and accounting. Given that accounting databases are used worldwide, particularly by non-technical people, there is an imminent need to develop models that could help extract information from accounting databases via natural language queries. In this resource paper, we aim to fill this gap by proposing a new large-scale Text-to-SQL dataset for the accounting and financial domain: BookSQL. The dataset consists of 100k natural language queries-SQL pairs, and accounting databases of 1 million records. We experiment with and analyze existing state-of-the-art models (including GPT-4) for the Text-to-SQL task on BookSQL. We find significant performance gaps, thus pointing towards developing more focused models for this domain.
Anthology ID:
2024.naacl-long.28
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
497–516
Language:
URL:
https://aclanthology.org/2024.naacl-long.28
DOI:
Bibkey:
Cite (ACL):
Rahul Kumar, Amar Raja Dibbu, Shrutendra Harsola, Vignesh Subrahmaniam, and Ashutosh Modi. 2024. BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domain. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 497–516, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domain (Kumar et al., NAACL 2024)
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PDF:
https://aclanthology.org/2024.naacl-long.28.pdf
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 2024.naacl-long.28.copyright.pdf