Text-To-SQL

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 )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 376–400 of 424 papers

Title	Date	Tasks	Status	Hype
DuoRAT: Towards Simpler Text-to-SQL Models	Oct 21, 2020	Text to SQLText-To-SQL	CodeCode Available	1
ColloQL: Robust Cross-Domain Text-to-SQL Over Search Queries	Oct 19, 2020	Data AugmentationText to SQL	CodeCode Available	0
Semantic Evaluation for Text-to-SQL with Distilled Test Suites	Oct 6, 2020	Text to SQLText-To-SQL	CodeCode Available	1
A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese	Oct 5, 2020	Language ModelingLanguage Modelling	CodeCode Available	1
A Tale of Two Linkings: Dynamically Gating between Schema Linking and Structural Linking for Text-to-SQL Parsing	Sep 30, 2020	Graph Neural NetworkSemantic Parsing	CodeCode Available	0
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing	Sep 29, 2020	Inductive BiasLanguage Modeling	CodeCode Available	1
Grounded Adaptation for Zero-shot Executable Semantic Parsing	Sep 16, 2020	Data AugmentationDialogue State Tracking	CodeCode Available	1
Hybrid Ranking Network for Text-to-SQL	Aug 11, 2020	Language ModelingLanguage Modelling	CodeCode Available	1
Photon: A Robust Cross-Domain Text-to-SQL System	Jul 30, 2020	Text to SQLText-To-SQL	—Unverified	0
Semantic Evaluation for Text-to-SQL with Distilled Test Suite	Jul 2, 2020	Semantic ParsingText to SQL	—Unverified	0
TableQA: a Large-Scale Chinese Text-to-SQL Dataset for Table-Aware SQL Generation	Jun 10, 2020	Text to SQLText-To-SQL	CodeCode Available	2
ValueNet: A Natural Language-to-SQL System that Learns from Database Information	May 29, 2020	Text-To-SQL	CodeCode Available	1
TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data	May 17, 2020	FormSemantic Parsing	CodeCode Available	1
Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback	May 5, 2020	FormSemantic Parsing	—Unverified	0
An Imitation Game for Learning Semantic Parsers from User Interaction	May 2, 2020	Imitation LearningText to SQL	CodeCode Available	1
Dataset and Enhanced Model for Eligibility Criteria-to-SQL Semantic Parsing	May 1, 2020	DiversitySemantic Parsing	—Unverified	0
RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases	Apr 7, 2020	Positionslot-filling	CodeCode Available	1
Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker	Feb 3, 2020	Text to SQLText-To-SQL	CodeCode Available	0
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers	Nov 10, 2019	RelationSemantic Parsing	CodeCode Available	1
Leveraging Adjective-Noun Phrasing Knowledge for Comparison Relation Prediction in Text-to-SQL	Nov 1, 2019	RelationRelation Prediction	—Unverified	0
Data-Anonymous Encoding for Text-to-SQL Generation	Nov 1, 2019	Text to SQLText-To-SQL	—Unverified	0
Graph Enhanced Cross-Domain Text-to-SQL Generation	Nov 1, 2019	Natural Language UnderstandingSemantic Parsing	—Unverified	0
Byte-Pair Encoding for Text-to-SQL Generation	Oct 20, 2019	Machine TranslationText to SQL	CodeCode Available	0
Content Enhanced BERT-based Text-to-SQL Generation	Oct 16, 2019	Code GenerationSemantic Parsing	CodeCode Available	0
Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study	Oct 11, 2019	Semantic ParsingText to SQL	CodeCode Available	1

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All datasets BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation)spider Spider 2.0 SParC KaggleDBQA SEDE SQL-Eval Text-To-SQL

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Human Performance	Execution Accurarcy (Human)	92.96	—	Unverified
2	XiYan-SQL	Execution Accuracy % (Test)	75.63	—	Unverified
3	DSAIR + GPT-4o	Execution Accuracy % (Test)	74.12	—	Unverified
4	CHASE-SQL + Gemini	Execution Accuracy % (Test)	74.06	—	Unverified
5	ExSL + granite-34b-code	Execution Accuracy % (Test)	73.17	—	Unverified
6	OpenSearch-SQL+ v2 + GPT-4o	Execution Accuracy % (Test)	72.28	—	Unverified
7	Distillery + GPT-4o	Execution Accuracy % (Test)	71.83	—	Unverified
8	Insights AI	Execution Accuracy % (Test)	70.26	—	Unverified
9	PURPLE + RED + GPT-4o	Execution Accuracy % (Test)	70.21	—	Unverified
10	MCTS-SQL	Execution Accuracy % (Test)	69.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	XiYan-SQL	Execution Accuracy (Test)	89.65	—	Unverified
2	PET-SQL	Execution Accuracy (Test)	87.6	—	Unverified
3	datagpt-sql-7B + InvalidSQL-Feedback	Execution Accuracy (Dev)	87.2	—	Unverified
4	DAIL-SQL + GPT-4 + Self-Consistency	Execution Accuracy (Test)	86.6	—	Unverified
5	DIN-SQL + GPT-4	Execution Accuracy (Test)	85.3	—	Unverified
6	datagpt-sql-7B	Execution Accuracy (Dev)	84.8	—	Unverified
7	MSc-SQL	Execution Accuracy (Test)	84.7	—	Unverified
8	MARLO + Claude 2.1	Execution Accuracy (Test)	84	—	Unverified
9	C3 + ChatGPT + Zero-Shot	Execution Accuracy (Test)	82.3	—	Unverified
10	code-davinci-002 175B (LEVER)	Execution Accuracy (Dev)	81.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spider-Agent + o1-preview	Success Rate	17.03	—	Unverified
2	Spider-Agent + GPT-4o	Success Rate	10.13	—	Unverified
3	Spider-Agent + Claude-3.5-Sonnect	Success Rate	9.02	—	Unverified
4	Spider-Agent + GPT-4	Success Rate	8.86	—	Unverified
5	Spider-Agent + Qwen2.5-72B	Success Rate	6.17	—	Unverified
6	Spider-Agent + DeepSeek-V2.5	Success Rate	5.22	—	Unverified
7	Spider-Agent + Gemini-Pro-1.5	Success Rate	2.53	—	Unverified
8	Spider-Agent + Llama-3.1-405B	Success Rate	2.21	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RASAT+PICARD	interaction match accuracy	45.2	—	Unverified
2	RAT-SQL-TC + GAP	interaction match accuracy	43.2	—	Unverified
3	HIE-SQL + GraPPa	interaction match accuracy	42.9	—	Unverified
4	RAT-SQL + SCoRe	interaction match accuracy	38.1	—	Unverified
5	EditSQL + BERT	interaction match accuracy	25.3	—	Unverified
6	GAZP + BERT	interaction match accuracy	23.5	—	Unverified
7	SyntaxSQL-con	interaction match accuracy	5.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	RAT-SQL	Exact Match (EM)	26.77	—	Unverified
2	Edit-SQL	Exact Match (EM)	11.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	T5-Large	PCM-F1 (dev)	48.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	XiYan-SQL	Execution Accuracy	69.86	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Orange-mini	0-shot MRR	74.17	—	Unverified