Text-To-SQL
134 papers with code • 6 benchmarks • 14 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
Spider-Realistic
SParC
CoSQL
KaggleDBQA
SEDE
MultiSpider
Most implemented papers
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM).
Natural SQL: Making SQL Easier to Infer from Natural Language Specifications
Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation.
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task
taoyds/syntaxsql
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In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task.
Using Database Rule for Weak Supervised Text-to-SQL Generation
We present a simple way to do the task of text-to-SQL problem with weak supervision.
A Pilot Study for Chinese SQL Semantic Parsing
The task of semantic parsing is highly useful for dialogue and question answering systems.
Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study
sunlab-osu/MISP
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IJCNLP 2019
As a promising paradigm, interactive semantic parsing has shown to improve both semantic parsing accuracy and user confidence in the results.
ValueNet: A Natural Language-to-SQL System that Learns from Database Information
In this paper we propose ValueNet light and ValueNet -- two end-to-end NL-to-SQL systems that incorporate values using the challenging Spider dataset.
TableQA: a Large-Scale Chinese Text-to-SQL Dataset for Table-Aware SQL Generation
InsaneLife/ChineseNLPCorpus
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Existing NL2SQL datasets assume that condition values should appear exactly in natural language questions and the queries are answerable given the table.
Hybrid Ranking Network for Text-to-SQL
In this paper, we study how to leverage pre-trained language models in Text-to-SQL.
SeqGenSQL -- A Robust Sequence Generation Model for Structured Query Language
We explore using T5 (Raffel et al. (2019)) to directly translate natural language questions into SQL statements.
