Code Generation On Wikisql

Métriques

Exact Match Accuracy

Execution Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

			Paper Title
NL2SQL-RULE	83.7	89.2	Content Enhanced BERT-based Text-to-SQL Generation
TypeSQL+TC (Yu et al., 2018)+	-	82.6	TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation
Tranx	68.6	78.6	TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
STAMP+RL (Sun et al., 2018)+	61.0	74.6	Semantic Parsing with Syntax- and Table-Aware SQL Generation
STAMP (Sun et al., 2018)+	60.7	74.4	Semantic Parsing with Syntax- and Table-Aware SQL Generation
TypeSQL (Yu et al., 2018)	-	73.5	TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation
PT-MAML (Huang et al., 2018)	62.8	68.0	Natural Language to Structured Query Generation via Meta-Learning
Bidirectional Attention for SQL Generation	69	62.5	Bidirectional Attention for SQL Generation
Seq2SQL (Zhong et al., 2017)	48.3	59.4	Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Seq2Seq (Zhong et al., 2017)	23.4	35.9	Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

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