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
- 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)
- PDF:
- https://preview.aclanthology.org/landing_page/2021.acl-long.176.pdf
- Code
- chiahsuan156/KaggleDBQA + additional community code
- Data
- KaggleDBQA, Spider-Realistic, WikiSQL