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Recently, context-dependent text-to-SQL semantic parsing which translatesnatural language into SQL in an interaction process has attracted a lot ofattention. Previous works leverage context-dependence information either frominteraction history utterances or the previous predicted SQL queries but failin taking advantage of both since of the mismatch between natural language andlogic-form SQL. In this work, we propose a History Information Enhancedtext-to-SQL model (HIE-SQL) to exploit context-dependence information from bothhistory utterances and the last predicted SQL query. In view of the mismatch,we treat natural language and SQL as two modalities and propose a bimodalpre-trained model to bridge the gap between them. Besides, we design aschema-linking graph to enhance connections from utterances and the SQL queryto the database schema. We show our history information enhanced methodsimprove the performance of HIE-SQL by a significant margin, which achieves newstate-of-the-art results on the two context-dependent text-to-SQL benchmarks,the SparC and CoSQL datasets, at the writing time.

HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing