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Question Generation from SQL Queries Improves Neural Semantic Parsing

arXiv 1808.06304 ↗First posted 20 Aug 2018cs.CLcited by 29

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Abstract (arXiv)

We study how to learn a semantic parser of state-of-the-art accuracy with less supervised training data. We conduct our study on WikiSQL, the largest hand-annotated semantic parsing dataset to date. First, we demonstrate that question generation is an effective method that empowers us to learn a state-of-the-art neural network based semantic parser with thirty percent of the supervised training data. Second, we show that applying question generation to the full supervised training data further improves the state-of-the-art model. In addition, we observe that there is a logarithmic relationship between the accuracy of a semantic parser and the amount of training data.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Aug 2018.

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

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  1. 2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scalecited by 90
  2. 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
  3. 2023ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systemscited by 42
  4. 2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 32
  5. 2022Importance of Synthesizing High-quality Data for Text-to-SQL Parsingcited by 27
  6. 2023Selective Demonstrations for Cross-domain Text-to-SQLcited by 21
  7. 2024SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Mergingcited by 16
  8. 2021On Robustness of Neural Semantic Parserscited by 14
  9. 2021From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decodingcited by 14
  10. 2021On The Ingredients of an Effective Zero-shot Semantic Parsercited by 13
  11. 2025Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQLcited by 11
  12. 2022Augmenting Multi-Turn Text-to-SQL Datasets with Self-Playcited by 9
17 more
  1. 2021Topic Transferable Table Question Answeringcited by 7
  2. 2022Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parserscited by 7
  3. 2022Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Frameworkcited by 6
  4. 2025Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data Annotationcited by 6
  5. 2024QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQLcited by 5
  6. 2023Mirror: A Natural Language Interface for Data Querying, Summarization, and Visualizationcited by 2
  7. 2024Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentationcited by 2
  8. 2023Controllable Data Augmentation for Context-Dependent Text-to-SQLcited by 1
  9. 2025REFORMER: A ChatGPT-Driven Data Synthesis Framework Elevating Text-to-SQL Modelscited by 1
  10. 2025SQL-Exchange: Transforming SQL Queries Across Domainscited by 1
  11. 2026EvolSQL: Structure-Aware Evolution for Scalable Text-to-SQL Data Synthesiscited by 1
  12. 2025Domain Specific Question to SQL Conversion with Embedded Data Balancing Techniquecited by 0
  13. 2025GradeSQL: Test-Time Inference with Outcome Reward Models for Text-to-SQL Generation from Large Language Modelscited by 0
  14. 2026RingSQL: Schema-Independent Synthetic Data Generation for Text-to-SQL Reinforcement Learningcited by 0
  15. 2026SemanticAgent: A Semantics-Aware Framework for Text-to-SQL Data Synthesiscited by 0
  16. 2026Knowledge Distillation for Low-Resource Open-source Text-to-SQL Modelcited by 0
  17. 2026COAL-SQL: Coverage-Guided Augmentation and Failure-Driven Learning for Text-to-SQL Post-Trainingcited by 0