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Learning to Synthesize Data for Semantic Parsing

arXiv 2104.05827 ↗First posted 12 Apr 2021cs.CLcited by 41

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Abstract (arXiv)

Synthesizing data for semantic parsing has gained increasing attention recently. However, most methods require handcrafted (high-precision) rules in their generative process, hindering the exploration of diverse unseen data. In this work, we propose a generative model which features a (non-neural) PCFG that models the composition of programs (e.g., SQL), and a BART-based translation model that maps a program to an utterance. Due to the simplicity of PCFG and pre-trained BART, our generative model can be efficiently learned from existing data at hand. Moreover, explicitly modeling compositions using PCFG leads to a better exploration of unseen programs, thus generate more diverse data. We evaluate our method in both in-domain and out-of-domain settings of text-to-SQL parsing on the standard benchmarks of GeoQuery and Spider, respectively. Our empirical results show that the synthesized data generated from our model can substantially help a semantic parser achieve better compositional and domain generalization.

Key facts. Cited by 41 papers in the map (data through 14 Oct 2026). Builds on 21 of its 32 references that are in the map. First posted on arXiv 12 Apr 2021.

Builds on

21 of 32 references.

References · 21

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  5. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
  6. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 424
  7. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  8. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  9. 2016Language to Logical Form with Neural Attentioncited by 158
  10. 2019Good-Enough Compositional Data Augmentationcited by 158
  11. 2018Improving Text-to-SQL Evaluation Methodologycited by 135
  12. 2020Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsingcited by 133
9 more
  1. 2016Data Recombination for Neural Semantic Parsingcited by 111
  2. 2017Learning a Neural Semantic Parser from User Feedbackcited by 99
  3. 2018TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generationcited by 88
  4. 2020Grounded Adaptation for Zero-shot Executable Semantic Parsingcited by 76
  5. 2018SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Taskcited by 74
  6. 2020RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databasescited by 63
  7. 2017Learning to Paraphrase for Question Answeringcited by 46
  8. 2020Meta-Learning for Domain Generalization in Semantic Parsingcited by 41
  9. 2019Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsingcited by 24

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 8; 2022: 13; 2023: 5; 2024: 4; 2025: 7; 2026: 4.

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  1. 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 235
  2. 2024CodeS: Towards Building Open-source Language Models for Text-to-SQLcited by 187
  3. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 98
  4. 2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scalecited by 91
  5. 2022A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directionscited by 52
  6. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  7. 2021Improving Compositional Generalization with Latent Structure and Data Augmentationcited by 46
  8. 2021Generate, Annotate, and Learn: NLP with Synthetic Textcited by 42
  9. 2022S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parserscited by 41
  10. 2022ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examplescited by 29
  11. 2022Importance of Synthesizing High-quality Data for Text-to-SQL Parsingcited by 28
  12. 2021Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalizationcited by 27
29 more
  1. 2023Selective Demonstrations for Cross-domain Text-to-SQLcited by 21
  2. 2023ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesiscited by 17
  3. 2024SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Mergingcited by 16
  4. 2021On The Ingredients of an Effective Zero-shot Semantic Parsercited by 13
  5. 2023Generating Data for Symbolic Language with Large Language Modelscited by 12
  6. 2021Hierarchical Neural Data Synthesis for Semantic Parsingcited by 11
  7. 2022Deep Learning Driven Natural Languages Text to SQL Query Conversion: A Surveycited by 11
  8. 2025Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQLcited by 11
  9. 2022Augmenting Multi-Turn Text-to-SQL Datasets with Self-Playcited by 9
  10. 2022Structurally Diverse Sampling for Sample-Efficient Training and Comprehensive Evaluationcited by 8
  11. 2022Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parserscited by 7
  12. 2022Compositional Generalization and Decomposition in Neural Program Synthesiscited by 6
  13. 2025Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data Annotationcited by 6
  14. 2021Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQLcited by 5
  15. 2024Improving Generalization in Semantic Parsing by Increasing Natural Language Variationcited by 4
  16. 2022Real-World Compositional Generalization with Disentangled Sequence-to-Sequence Learningcited by 3
  17. 2021Linking-Enhanced Pre-Training for Table Semantic Parsingcited by 1
  18. 2023Controllable Data Augmentation for Context-Dependent Text-to-SQLcited by 1
  19. 2023Reranking for Natural Language Generation from Logical Forms: A Study based on Large Language Modelscited by 1
  20. 2025REFORMER: A ChatGPT-Driven Data Synthesis Framework Elevating Text-to-SQL Modelscited by 1
  21. 2025SQL-Exchange: Transforming SQL Queries Across Domainscited by 1
  22. 2026EvolSQL: Structure-Aware Evolution for Scalable Text-to-SQL Data Synthesiscited by 1
  23. 2022A Short Survey of Systematic Generalizationcited by 0
  24. 2024EzSQL: An SQL intermediate representation for improving SQL-to-text Generationcited by 0
  25. 2025Domain Specific Question to SQL Conversion with Embedded Data Balancing Techniquecited by 0
  26. 2025Skeletons Matter: Dynamic Data Augmentation for Text-to-Querycited by 0
  27. 2026RingSQL: Schema-Independent Synthetic Data Generation for Text-to-SQL Reinforcement Learningcited by 0
  28. 2026Reliable Answers for Recurring Questions: Boosting Text-to-SQL Accuracy with Template Constrained Decodingcited by 0
  29. 2026Knowledge Distillation for Low-Resource Open-source Text-to-SQL Modelcited by 0