Text-To-Sql
State-of-the-art leaderboards
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Greatest papers with code
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
ICLR 2018 • salesforce/WikiSQL
A significant amount of the world's knowledge is stored in relational databases.
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
Existing state-of-the-art approaches rely on reinforcement learning to reward the decoder when it generates any of the equivalent serializations.
SOTA for Text-To-Sql on WikiSQL
Improving Text-to-SQL Evaluation Methodology
ACL 2018 • jkkummerfeld/text2sql-data
Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work.
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
We define a new complex and cross-domain semantic parsing and text-to-SQL task where different complex SQL queries and databases appear in train and test sets.
SOTA for Semantic Parsing on spider
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task
• taoyds/syntaxsql
• 
In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task.
TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation
Interacting with relational databases through natural language helps users of any background easily query and analyze a vast amount of data.
Robust Text-to-SQL Generation with Execution-Guided Decoding
We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries.
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
We present a neural approach called IRNet for complex and cross-domain Text-to-SQL.