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This paper delves into the recent advances in Natural Language to SQL (NL-to-SQL) query generation, with particular attention paid to techniques such as Seq2SQL, Sketch Decoder Models, BERT-based Sketch, and Transformer-based Models. We offer an in-depth analysis of these techniques alongside its various disadvantages to shed light on the state of NL-to-SQL creation today. Through the integration of results from many studies, our study provides a comprehensive overview of significant developments and new avenues for research. This analysis of automating the translation of natural language queries into SQL statements is a useful tool for researchers, with important ramifications for data analytics and human–computer interaction.

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Advancements in SQL Query Generation Schemes in the Field of Natural Language Processing: A Comprehensive Overview

  • 会议论文

  • 发表日期：

    2026年

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  • Md Sakir Ahmed,
  • Abhishek Khajuria,
  • Manas Jyoti Gogoi,
  • Palash Jyoti Goswami,
  • Penugonda Gowri Shankar,
  • Pranjal Barman,
  • Debajit Sarma

摘要

This paper delves into the recent advances in Natural Language to SQL (NL-to-SQL) query generation, with particular attention paid to techniques such as Seq2SQL, Sketch Decoder Models, BERT-based Sketch, and Transformer-based Models. We offer an in-depth analysis of these techniques alongside its various disadvantages to shed light on the state of NL-to-SQL creation today. Through the integration of results from many studies, our study provides a comprehensive overview of significant developments and new avenues for research. This analysis of automating the translation of natural language queries into SQL statements is a useful tool for researchers, with important ramifications for data analytics and human–computer interaction.