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Artificial Neural Networks for Text-to-SQL Task: State of the Art

Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 684)

Abstract

Databases store a significant amount of data from the world, however, to access this data, users must understand a query language such as SQL. In order to facilitate this task and to make the interaction with databases possible for all the world, researches has recently appeared to approach systems that understand the natural language questions and automatically convert them into SQL queries. The purpose of this article is to provide the state of the art text-to-SQL task in which we present the main models and existing solutions based on Artificial Neural Networks (ANN), precisely on Deep Learning (DL) and Natural Language Processing (NLP). We also specify the experimental settings of each approach, their limits as well as a comparison of the best existing ones.

Keywords

  • ANN
  • DL
  • NLP
  • SQL
  • Text-to-SQL

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Correspondence to Youssef Mellah .

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Mellah, Y., Ettifouri, H.E., Bouchentouf, T., Belkasmi, M.G. (2020). Artificial Neural Networks for Text-to-SQL Task: State of the Art. In: El Moussati, A., Kpalma, K., Ghaouth Belkasmi, M., Saber, M., Guégan, S. (eds) Advances in Smart Technologies Applications and Case Studies. SmartICT 2019. Lecture Notes in Electrical Engineering, vol 684. Springer, Cham. https://doi.org/10.1007/978-3-030-53187-4_61

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  • DOI: https://doi.org/10.1007/978-3-030-53187-4_61

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