Dechen Zhan


2022

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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 | Jian-Guang Lou
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

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.