mlsanityData to 7 Oct 2026

Semantic Parsing with Syntax- and Table-Aware SQL Generation

arXiv 1804.08338 ↗First posted 23 Apr 2018cs.CLcited by 17

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Abstract (arXiv)

We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query word-by-word, however, a large portion of the generated results are incorrect or not executable due to the mismatch between question words and table contents. Our approach addresses this problem by considering the structure of table and the syntax of SQL language. The quality of the generated SQL query is significantly improved through (1) learning to replicate content from column names, cells or SQL keywords; and (2) improving the generation of WHERE clause by leveraging the column-cell relation. Experiments are conducted on WikiSQL, a recently released dataset with the largest question-SQL pairs. Our approach significantly improves the state-of-the-art execution accuracy from 69.0% to 74.4%.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Apr 2018.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 5; 2024: 2; 2025: 2; 2026: 1.

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  1. 2022A Survey on Table Question Answering: Recent Advancescited by 41
  2. 2023Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specificationcited by 38
  3. 2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 32
  4. 2021Improving Text-to-SQL with Schema Dependency Learningcited by 25
  5. 2022UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQLcited by 20
  6. 2025Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errorscited by 14
  7. 2023Testing the Limits of Unified Sequence to Sequence LLM Pretraining on Diverse Table Data Taskscited by 10
  8. 2023Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversationscited by 9
  9. 2024End-to-end Text-to-SQL Generation within an Analytics Insight Enginecited by 7
  10. 2022Speech-to-SQL: Towards Speech-driven SQL Query Generation From Natural Language Questioncited by 6
  11. 2021Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQLcited by 5
  12. 2023On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Softwarecited by 3
5 more
  1. 2024ERATTA: Extreme RAG for Table To Answers with Large Language Modelscited by 1
  2. 2025JudgeSQL: Reasoning over SQL Candidates with Weighted Consensus Tournamentcited by 1
  3. 2026EvolSQL: Structure-Aware Evolution for Scalable Text-to-SQL Data Synthesiscited by 1
  4. 2021Self-supervised Text-to-SQL Learning with Header Alignment Trainingcited by 0
  5. 2023Feature Representation Learning for NL2SQL Generation Based on Coupling and Decouplingcited by 0