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GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing

arXiv 2009.13845 ↗First posted 29 Sep 2020cs.CL cs.AIcited by 171

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Abstract (arXiv)

We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data. We construct synthetic question-SQL pairs over high-quality tables via a synchronous context-free grammar (SCFG) induced from existing text-to-SQL datasets. We pre-train our model on the synthetic data using a novel text-schema linking objective that predicts the syntactic role of a table field in the SQL for each question-SQL pair. To maintain the model's ability to represent real-world data, we also include masked language modeling (MLM) over several existing table-and-language datasets to regularize the pre-training process. On four popular fully supervised and weakly supervised table semantic parsing benchmarks, GraPPa significantly outperforms RoBERTa-large as the feature representation layers and establishes new state-of-the-art results on all of them.

Key facts. Cited by 171 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Sep 2020.

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Citing papers per year: 2021: 33; 2022: 41; 2023: 39; 2024: 31; 2025: 18; 2026: 9.

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  1. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 465
  2. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  3. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 233
  4. 2022Binding Language Models in Symbolic Languagescited by 224
  5. 2023RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQLcited by 191
  6. 2024CodeS: Towards Building Open-source Language Models for Text-to-SQLcited by 183
  7. 2021FeTaQA: Free-form Table Question Answeringcited by 174
  8. 2021Constrained Language Models Yield Few-Shot Semantic Parserscited by 162
  9. 2024CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQLcited by 132
  10. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  11. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
  12. 2022Large Language Models are few(1)-shot Table Reasonerscited by 116
38 more
  1. 2021KaggleDBQA: Realistic Evaluation of Text-to-SQL Parserscited by 105
  2. 2024A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discoverycited by 96
  3. 2022TableFormer: Robust Transformer Modeling for Table-Text Encodingcited by 90
  4. 2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scalecited by 90
  5. 2024TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarioscited by 76
  6. 2023Exploring Chain-of-Thought Style Prompting for Text-to-SQLcited by 69
  7. 2022Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environmentscited by 57
  8. 2023How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settingscited by 57
  9. 2023Generative Table Pre-training Empowers Models for Tabular Predictioncited by 55
  10. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 54
  11. 2022A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directionscited by 51
  12. 2023Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustnesscited by 50
  13. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  14. 2021Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Datacited by 43
  15. 2021SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQLcited by 43
  16. 2022Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Taskscited by 42
  17. 2023ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systemscited by 42
  18. 2021Learning to Synthesize Data for Semantic Parsingcited by 41
  19. 2022S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parserscited by 41
  20. 2024UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-world Document Analysiscited by 40
  21. 2025XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQLcited by 40
  22. 2021Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skillscited by 39
  23. 2024TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decompositioncited by 39
  24. 2022SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Modelscited by 38
  25. 2021ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parsercited by 35
  26. 2021Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Trackingcited by 35
  27. 2024E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQLcited by 35
  28. 2021NL-EDIT: Correcting semantic parse errors through natural language interactioncited by 33
  29. 2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 32
  30. 2022XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsingcited by 32
  31. 2023MultiTabQA: Generating Tabular Answers for Multi-Table Question Answeringcited by 32
  32. 2022Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsingcited by 30
  33. 2022OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answeringcited by 29
  34. 2024StructLM: Towards Building Generalist Models for Structured Knowledge Groundingcited by 29
  35. 2021Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalizationcited by 27
  36. 2022Importance of Synthesizing High-quality Data for Text-to-SQL Parsingcited by 27
  37. 2023Natural Language Interfaces for Tabular Data Querying and Visualization: A Surveycited by 27
  38. 2023TempTabQA: Temporal Question Answering for Semi-Structured Tablescited by 27

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