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Text To Sql On Sparc
Métriques
interaction match accuracy
question match accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
| Paper Title | |||
|---|---|---|---|
| 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 |
| HIE-SQL + GraPPa | 42.9 | 64.6 | HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing |
| RAT-SQL + SCoRe | 38.1 | 62.4 | SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing |
| EditSQL + BERT | 25.3 | 47.9 | Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions |
| GAZP + BERT | 23.5 | 45.9 | Grounded Adaptation for Zero-shot Executable Semantic Parsing |
| SyntaxSQL-con | 5.2 | 20.2 | SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task |
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