105 papers with code • 5 benchmarks • 11 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 )


Use these libraries to find Text-To-SQL models and implementations
4 papers

Most implemented papers

SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

salesforce/WikiSQL ICLR 2018

Existing state-of-the-art approaches rely on reinforcement learning to reward the decoder when it generates any of the equivalent serializations.

Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation

zhanzecheng/IRNet ACL 2019

We present a neural approach called IRNet for complex and cross-domain Text-to-SQL.

SParC: Cross-Domain Semantic Parsing in Context

ryanzhumich/editsql 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.

Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task

taoyds/spider EMNLP 2018

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.

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases

ryanzhumich/editsql IJCNLP 2019

We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems.

Content Enhanced BERT-based Text-to-SQL Generation

guotong1988/NL2SQL-BERT 16 Oct 2019

We present a simple methods to leverage the table content for the BERT-based model to solve the text-to-SQL problem.

RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers

Microsoft/rat-sql 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.

Semantic Evaluation for Text-to-SQL with Distilled Test Suites

ruiqi-zhong/TestSuiteEval EMNLP 2020

We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models.