Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge

• URL: http://arxiv.org/abs/2301.01067v1
• Date: Tue, 3 Jan 2023 12:37:47 GMT
• Title: Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge
• Authors: Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan, Jian-Guang Lou
• Abstract summary: We build a new Chinese benchmark Know consisting of domain-specific questions covering various domains. We then address this problem by presenting 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.
• Score: 54.85168428642474
• License: http://creativecommons.org/licenses/by/4.0/
• 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 Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting 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.

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