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

16 Nov 2021ACL ARR November 2021 11archive 2025-07-28

Anonymous

Recently, context-dependent text-to-SQL semantic parsing which translates natural language into SQL in an interaction process has attracted a lot of attentions. Previous works leverage context dependence information either from interaction history utterances or previous predicted queries but fail in taking advantage of both of them since of the mismatch between the 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 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 two context-dependent text-to-SQL benchmarks, the SparC and CoSQL datasets, at the writing time.

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Tasks

Dialogue State TrackingSemantic ParsingText to SQLText-To-SQL

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Dialogue State Tracking CoSQL HIE-SQL + GraPPa interaction match accuracy 24.6 #3 of 9 Archive leaderboard report
Dialogue State Tracking CoSQL HIE-SQL + GraPPa question match accuracy 53.9 #3 of 9 Archive leaderboard report
Text-To-SQL SParC HIE-SQL + GraPPa interaction match accuracy 42.9 #3 of 7 Archive leaderboard report
Text-To-SQL SParC HIE-SQL + GraPPa question match accuracy 64.6 #3 of 7 Archive leaderboard report

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