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

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 K NOW SQL 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 (R E G ROU P) to leverage this formulaic knowledge during parsing. Experiments using R E G ROU P demonstrate a significant 28 . 2 % improvement overall on K NOW SQL.