KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers

Chia-Hsuan Lee, Oleksandr Polozov, Matthew Richardson


Abstract
The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains. Recently, large-scale datasets such as Spider and WikiSQL facilitated novel modeling techniques for text-to-SQL parsing, improving zero-shot generalization to unseen databases. In this work, we examine the challenges that still prevent these techniques from practical deployment. First, we present KaggleDBQA, a new cross-domain evaluation dataset of real Web databases, with domain-specific data types, original formatting, and unrestricted questions. Second, we re-examine the choice of evaluation tasks for text-to-SQL parsers as applied in real-life settings. Finally, we augment our in-domain evaluation task with database documentation, a naturally occurring source of implicit domain knowledge. We show that KaggleDBQA presents a challenge to state-of-the-art zero-shot parsers but a more realistic evaluation setting and creative use of associated database documentation boosts their accuracy by over 13.2%, doubling their performance.
Anthology ID:
2021.acl-long.176
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2261–2273
Language:
URL:
https://aclanthology.org/2021.acl-long.176
DOI:
10.18653/v1/2021.acl-long.176
Bibkey:
Cite (ACL):
Chia-Hsuan Lee, Oleksandr Polozov, and Matthew Richardson. 2021. KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2261–2273, Online. Association for Computational Linguistics.
Cite (Informal):
KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers (Lee et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.176.pdf
Video:
 https://aclanthology.org/2021.acl-long.176.mp4
Code
 chiahsuan156/KaggleDBQA +  additional community code
Data
KaggleDBQASpider-RealisticWikiSQL