Text-To-SQL
193 papers with code • 10 benchmarks • 20 datasets
Text-to-SQL is a task in natural language processing (NLP) where the goal is to automatically generate SQL queries from natural language text. The task involves converting the text input into a structured representation and then using this representation to generate a semantically correct SQL query that can be executed on a database.
( Image credit: SyntaxSQLNet )
Benchmarks
These leaderboards are used to track progress in Text-To-SQL
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Libraries
Use these libraries to find Text-To-SQL models and implementationsDatasets
KITTI
Spider-Realistic
SParC
CoSQL
KaggleDBQA
SEDE
Most implemented papers
Non-local Neural Networks
facebookresearch/video-nonlocal-net
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CVPR 2018
Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time.
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
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.
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.
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.
Content Enhanced BERT-based Text-to-SQL Generation
We present a simple methods to leverage the table content for the BERT-based model to solve the text-to-SQL problem.
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
On the other hand, we implement the ICL approach with an example selection method based on named entity recognition to prevent overemphasis on entities.
SParC: Cross-Domain Semantic Parsing in Context
ryanzhumich/editsql
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ACL 2019
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.
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
Microsoft/rat-sql
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ACL 2020
The generalization challenge lies in (a) encoding the database relations in an accessible way for the semantic parser, and (b) modeling alignment between database columns and their mentions in a given query.
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
ryanzhumich/editsql
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IJCNLP 2019
We focus on the cross-domain context-dependent text-to-SQL generation task.
