Text-To-Sql
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
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Greatest papers with code
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
ICLR 2018 • salesforce/WikiSQL
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
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.
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
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.
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.
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
• ryanzhumich/sparc_atis_pytorch
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We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems.
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
• ryanzhumich/sparc_atis_pytorch
•
We focus on the cross-domain context-dependent text-to-SQL generation task.
SParC: Cross-Domain Semantic Parsing in Context
ACL 2019
• ryanzhumich/sparc_atis_pytorch
•
The best model obtains an exact match accuracy of 20. 2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset present significant challenges for future research.