mlsanityData to 14 Oct 2026

S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers

arXiv 2203.06958 ↗First posted 14 Mar 2022cs.CLcited by 41

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Abstract (arXiv)

The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing. The state-of-the-art graph-based encoder has been successfully used in this task but does not model the question syntax well. In this paper, we propose S2SQL, injecting Syntax to question-Schema graph encoder for Text-to-SQL parsers, which effectively leverages the syntactic dependency information of questions in text-to-SQL to improve the performance. We also employ the decoupling constraint to induce diverse relational edge embedding, which further improves the network's performance. Experiments on the Spider and robustness setting Spider-Syn demonstrate that the proposed approach outperforms all existing methods when pre-training models are used, resulting in a performance ranks first on the Spider leaderboard.

Key facts. Cited by 41 papers in the map (data through 14 Oct 2026). Builds on 29 of its 42 references that are in the map. First posted on arXiv 14 Mar 2022.

Builds on

29 of 42 references.

References · 29

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,626
  4. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  5. 2018Self-Attention with Relative Position Representationscited by 1,120
  6. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 424
  7. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 411
  8. 2020TaBERT: Pretraining for Joint Understanding of Textual and Tabular Datacited by 398
  9. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  10. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  11. 2020Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsingcited by 133
  12. 2020Structure-Grounded Pretraining for Text-to-SQLcited by 126
17 more
  1. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  2. 2021Towards Robustness of Text-to-SQL Models against Synonym Substitutioncited by 110
  3. 2017Learning a Neural Semantic Parser from User Feedbackcited by 99
  4. 2020SmBoP: Semi-autoregressive Bottom-up Semantic Parsingcited by 97
  5. 2019Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questionscited by 80
  6. 2020Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Trainingcited by 65
  7. 2020RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databasescited by 63
  8. 2019Global Reasoning over Database Structures for Text-to-SQL Parsingcited by 43
  9. 2020Meta-Learning for Domain Generalization in Semantic Parsingcited by 41
  10. 2021Learning to Synthesize Data for Semantic Parsingcited by 41
  11. 2021ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parsercited by 35
  12. 2019Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programscited by 26
  13. 2021Improving Text-to-SQL with Schema Dependency Learningcited by 25
  14. 2020Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-rankercited by 12
  15. 2019Dating Documents using Graph Convolution Networkscited by 9
  16. 2019Syntax-Enhanced Self-Attention-Based Semantic Role Labelingcited by 8
  17. 2020Self-Orthogonality Module: A Network Architecture Plug-in for Learning Orthogonal Filterscited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 12; 2023: 18; 2024: 5; 2025: 5; 2026: 1.

41 papers.

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  1. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  2. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 195
  3. 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoningcited by 178
  4. 2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
  5. 2022RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQLcited by 85
  6. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 56
  7. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  8. 2023SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)cited by 42
  9. 2024PURPLE: Making a Large Language Model a Better SQL Writercited by 34
  10. 2022Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsingcited by 30
  11. 2022Duplex Conversation: Towards Human-like Interaction in Spoken Dialogue Systemscited by 28
  12. 2022Importance of Synthesizing High-quality Data for Text-to-SQL Parsingcited by 28
29 more
  1. 2025Reliable Text-to-SQL with Adaptive Abstentioncited by 20
  2. 2022STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsingcited by 19
  3. 2023Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrievalcited by 16
  4. 2022UniRPG: Unified Discrete Reasoning over Table and Text as Program Generationcited by 13
  5. 2023Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chaincited by 12
  6. 2022SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parserscited by 7
  7. 2022Towards Generalizable and Robust Text-to-SQL Parsingcited by 7
  8. 2023SPRING: Situated Conversation Agent Pretrained with Multimodal Questions from Incremental Layout Graphcited by 6
  9. 2022TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pagescited by 4
  10. 2023HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generationcited by 4
  11. 2023A Multilingual Translator to SQL with Database Schema Pruning to Improve Self-Attentioncited by 3
  12. 2024Enhancing LLM Fine-tuning for Text-to-SQLs by SQL Quality Measurementcited by 3
  13. 2022Graph-to-Text Generation with Dynamic Structure Pruningcited by 2
  14. 2023Plan-then-Seam: Towards Efficient Table-to-Text Generationcited by 2
  15. 2023Conversational Semantic Parsing using Dynamic Context Graphscited by 2
  16. 2023T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsingcited by 2
  17. 2024Cooperative SQL Generation for Segmented Databases By Using Multi-functional LLM Agentscited by 2
  18. 2025End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generationcited by 2
  19. 2022DocuT5: Seq2seq SQL Generation with Table Documentationcited by 1
  20. 2023On the Structural Generalization in Text-to-SQLcited by 1
  21. 2023HopPG: Self-Iterative Program Generation for Multi-Hop Question Answering over Heterogeneous Knowledgecited by 1
  22. 2023Decoupling SQL Query Hardness Parsing for Text-to-SQLcited by 1
  23. 2025SQLord: A Robust Enterprise Text-to-SQL Solution via Reverse Data Generation and Workflow Decompositioncited by 1
  24. 2023CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Datasetcited by 0
  25. 2023Translating Natural Language Queries to SQL Using the T5 Modelcited by 0
  26. 2024RH-SQL: Refined Schema and Hardness Prompt for Text-to-SQLcited by 0
  27. 2025Prompt Engineering Techniques for Context-dependent Text-to-SQL in Arabiccited by 0
  28. 2025Bridging Global Intent with Local Details: A Hierarchical Representation Approach for Semantic Validation in Text-to-SQLcited by 0
  29. 2026DRL: A Deterministic Relational Middleware Layer for Transaction-Safe Enterprise NL2SQL Under Schema-Graph Scalingcited by 0