HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing
This work addresses context-dependent text-to-SQL parsing, a specific challenge in natural language processing for database interactions, with incremental improvements over prior methods.
The paper tackled the problem of context-dependent text-to-SQL semantic parsing by proposing HIE-SQL, a model that exploits both history utterances and previous SQL queries, achieving new state-of-the-art results on SparC and CoSQL benchmarks.
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.