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Text2SQLNet: Syntax Type-Aware Tree Networks for Text-to-SQL

Part of the Learning and Analytics in Intelligent Systems book series (LAIS,volume 7)

Abstract

Building natural language interfaces to relational databases is an important and challenging problem in natural language processing (NLP), it requires a system that is able to understand natural language questions and generate corresponding SQL queries. In this paper, we present our idea of using type information and database content to better understand rare entities and numbers in natural language questions, in order to improve the model SyntaxSQLNet as the state of the art in Text-to-SQL task. We also present the global architecture and techniques that can be used in the implementation of our Neural Network (NN) model Text2SQLNet, with the integration of our idea that consists of using type information to better understand rare entities and numbers in natural language questions. We can also use the database content to better understand the user query if it is not well-formed. The implementation of this idea can further improve performance in the Text-to-SQL task.

Keywords

  • NLP
  • SQL
  • NN
  • SyntaxSQLNet
  • Text-to-SQL
  • Text2SQLNet

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Notes

  1. 1.

    https://developers.google.com/freebase/.

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Correspondence to Youssef Mellah , El Hassane Ettifouri , Toumi Bouchentouf or Mohammed Ghaouth Belkasmi .

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Mellah, Y., Ettifouri, E.H., Bouchentouf, T., Belkasmi, M.G. (2020). Text2SQLNet: Syntax Type-Aware Tree Networks for Text-to-SQL. In: Serrhini, M., Silva, C., Aljahdali, S. (eds) Innovation in Information Systems and Technologies to Support Learning Research. EMENA-ISTL 2019. Learning and Analytics in Intelligent Systems, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-36778-7_48

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