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Code Generation On Wikisql

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Exact Match Accuracy
Execution Accuracy

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Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Paper Title
NL2SQL-RULE83.789.2Content Enhanced BERT-based Text-to-SQL Generation
TypeSQL+TC (Yu et al., 2018)+-82.6TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation
Tranx68.678.6TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
STAMP+RL (Sun et al., 2018)+61.074.6Semantic Parsing with Syntax- and Table-Aware SQL Generation
STAMP (Sun et al., 2018)+60.774.4Semantic Parsing with Syntax- and Table-Aware SQL Generation
TypeSQL (Yu et al., 2018)-73.5TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation
PT-MAML (Huang et al., 2018)62.868.0Natural Language to Structured Query Generation via Meta-Learning
Bidirectional Attention for SQL Generation6962.5Bidirectional Attention for SQL Generation
Seq2SQL (Zhong et al., 2017)48.359.4Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Seq2Seq (Zhong et al., 2017)23.435.9Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
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Code Generation On Wikisql | SOTA | HyperAI