Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge
Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan
Abstract
In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by representing formulaic knowledge rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.
BibTeX
@inproceedings{dou-etal-2022-towards,
title = "Towards Knowledge-Intensive Text-to-{SQL} Semantic Parsing with Formulaic Knowledge",
author = "Dou, Longxu and
Gao, Yan and
Liu, Xuqi and
Pan, Mingyang and
Wang, Dingzirui and
Che, Wanxiang and
Zhan, Dechen and
Kan, Min-Yen and
Lou, Jian-Guang",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.350/",
doi = "10.18653/v1/2022.emnlp-main.350",
pages = "5240--5253"
}