Text-To-SQL
136 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
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.
IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation
headacheboy/IGSQL
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EMNLP 2020
Our model outperforms previous state-of-the-art model by a large margin and achieves new state-of-the-art results on the two datasets.
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
salesforce/TabularSemanticParsing
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Findings of the Association for Computational Linguistics 2020
We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing.
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
huybery/r2sql
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Semantic parsing has long been a fundamental problem in natural language processing.
Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations
Sequence-to-sequence (seq2seq) models are prevalent in semantic parsing, but have been found to struggle at out-of-distribution compositional generalization.
KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers
The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains.
Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing
alibabaresearch/damo-convai
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The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i. e., properly recognizing mentions of unseen columns or tables when generating SQLs.
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries.
Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing
hyan5/learning_to_simulate_nl_feedback
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In this work, we propose a new task of simulating NL feedback for interactive semantic parsing.
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?
trais-lab/llm-structured-data
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We aim to understand why the incorporation of structural information inherent in graph data can improve the prediction performance of LLMs.
