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