Text To Sql On Sparc
Metrics
interaction match accuracy
question match accuracy
Results
Performance results of various models on this benchmark
Model Name | interaction match accuracy | question match accuracy | Paper Title | Repository |
|---|---|---|---|---|
| RAT-SQL + SCoRe | 38.1 | 62.4 | SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing | - |
| HIE-SQL + GraPPa | 42.9 | 64.6 | HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing | - |
| EditSQL + BERT | 25.3 | 47.9 | Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions | |
| SyntaxSQL-con | 5.2 | 20.2 | SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task | |
| RASAT+PICARD | 45.2 | 67.7 | RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL | |
| RAT-SQL-TC + GAP | 43.2 | 65.7 | Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQL | |
| GAZP + BERT | 23.5 | 45.9 | Grounded Adaptation for Zero-shot Executable Semantic Parsing |
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