Code Generation on WikiSQL

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Execution Accuracy

Rank	Model	Execution Accuracy	Exact Match Accuracy	Paper	Code	Year
1	NL2SQL-RULE	89.2	83.7	Content Enhanced BERT-based Text-to-SQL Generation	guotong1988/NL2SQL-RULE · guotong1988/NL2SQL-BERT · shivam017arora/Conversational-BI · +2	2019
2	TypeSQL+TC (Yu et al., 2018)+	82.6	–	TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation	taoyds/typesql	2018
3	Tranx	78.6	68.6	TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation	pcyin/tranX · DeepLearnXMU/CG-RL · yuxiang2/CoNaLa · +1	2018
4	STAMP+RL (Sun et al., 2018)+	74.6	61.0	Semantic Parsing with Syntax- and Table-Aware SQL Generation		2018
5	STAMP (Sun et al., 2018)+	74.4	60.7	Semantic Parsing with Syntax- and Table-Aware SQL Generation		2018
6	TypeSQL (Yu et al., 2018)	73.5	–	TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation	taoyds/typesql	2018
7	PT-MAML (Huang et al., 2018)	68.0	62.8	Natural Language to Structured Query Generation via Meta-Learning	Microsoft/PointerSQL	2018
8	Bidirectional Attention for SQL Generation	62.5	69	Bidirectional Attention for SQL Generation	guotong1988/NL2SQL · openbotai/nl2sql	2017
9	Seq2SQL (Zhong et al., 2017)	59.4	48.3	Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning	salesforce/WikiSQL · xiaojunxu/SQLNet · tiwarikajal/Seq2SQL- · +12	2017
10	Seq2Seq (Zhong et al., 2017)	35.9	23.4	Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning	salesforce/WikiSQL · xiaojunxu/SQLNet · tiwarikajal/Seq2SQL- · +12	2017