mlsanityData to 7 Oct 2026

Improving Text-to-SQL with Schema Dependency Learning

arXiv 2103.04399 ↗First posted 7 Mar 2021cs.CLcited by 25

Not on the map. Why?

Abstract (arXiv)

Text-to-SQL aims to map natural language questions to SQL queries. The sketch-based method combined with execution-guided (EG) decoding strategy has shown a strong performance on the WikiSQL benchmark. However, execution-guided decoding relies on database execution, which significantly slows down the inference process and is hence unsatisfactory for many real-world applications. In this paper, we present the Schema Dependency guided multi-task Text-to-SQL model (SDSQL) to guide the network to effectively capture the interactions between questions and schemas. The proposed model outperforms all existing methods in both the settings with or without EG. We show the schema dependency learning partially cover the benefit from EG and alleviates the need for it. SDSQL without EG significantly reduces time consumption during inference, sacrificing only a small amount of performance and provides more flexibility for downstream applications.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 28 of its 39 references that are in the map. First posted on arXiv 7 Mar 2021.

Builds on

28 of 39 references.

References · 28

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  5. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
  6. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  7. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  8. 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
  9. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 425
  10. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  11. 2016Deep Biaffine Attention for Neural Dependency Parsingcited by 334
  12. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
16 more
  1. 2019CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databasescited by 180
  2. 2016Language to Logical Form with Neural Attentioncited by 158
  3. 2019SParC: Cross-Domain Semantic Parsing in Contextcited by 141
  4. 2017SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learningcited by 135
  5. 2018Coarse-to-Fine Decoding for Neural Semantic Parsingcited by 110
  6. 2017Learning a Neural Semantic Parser from User Feedbackcited by 98
  7. 2019A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualizationcited by 88
  8. 2018TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generationcited by 81
  9. 2020RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databasescited by 63
  10. 2018Robust Text-to-SQL Generation with Execution-Guided Decodingcited by 51
  11. 2018Simpler but More Accurate Semantic Dependency Parsingcited by 46
  12. 2020Hybrid Ranking Network for Text-to-SQLcited by 33
  13. 2020Program Enhanced Fact Verification with Verbalization and Graph Attention Networkcited by 29
  14. 2019X-SQL: reinforce schema representation with contextcited by 22
  15. 2018Semantic Parsing with Syntax- and Table-Aware SQL Generationcited by 17
  16. 2020Mention Extraction and Linking for SQL Query Generationcited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 7; 2023: 5; 2024: 3; 2025: 4; 2026: 3.

25 papers.

Sort:

By area:

  1. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
  2. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  3. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
  4. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
  5. 2021Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalizationcited by 92
  6. 2022A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directionscited by 51
  7. 2024Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generationcited by 49
  8. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  9. 2022S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parserscited by 41
  10. 2022Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsingcited by 30
  11. 2021SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoisingcited by 27
  12. 2024MAG-SQL: Multi-Agent Generative Approach with Soft Schema Linking and Iterative Sub-SQL Refinement for Text-to-SQLcited by 22
13 more
  1. 2025STaR-SQL: Self-Taught Reasoner for Text-to-SQLcited by 16
  2. 2022SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parserscited by 7
  3. 2022Towards Generalizable and Robust Text-to-SQL Parsingcited by 7
  4. 2022Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsingcited by 5
  5. 2021Lyra: A Benchmark for Turducken-Style Code Generationcited by 4
  6. 2025Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunitiescited by 4
  7. 2025SDE-SQL: Enhancing Text-to-SQL Generation in Large Language Models via Self-Driven Exploration with SQL Probescited by 2
  8. 2025Squrve: A Unified and Modular Framework for Complex Real-World Text-to-SQL Taskscited by 1
  9. 2026Extending Confidence-Based Text2Cypher with Grammar and Schema Aware Filteringcited by 1
  10. 2023(Vector) Space is Not the Final Frontier: Product Search as Program Synthesiscited by 0
  11. 2023Feature Representation Learning for NL2SQL Generation Based on Coupling and Decouplingcited by 0
  12. 2026The Nuts and Bolts of Natural Language to SQL Translation: A Systematic Analysis of Model Pipeline Optimisation Approaches and their Interactionscited by 0
  13. 2026CHS-SQL: A Text-to-SQL approach based on Confidence-Guided Heuristic Search Schema Linking processcited by 0