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An Optimized NL2SQL System for Enterprise Data Mart

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track (ECML PKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12979))

Abstract

Natural language interfaces to databases is a growing field that enables end users to interact with relational databases without technical database skills. These interfaces solve the problem of synthesizing SQL queries based on natural language input from the user. There are considerable research interests around the topic but there are few systems to date that are deployed on top of an active enterprise data mart. We present our NL2SQL system designed for the banking sector, which can generate a SQL query from a user’s natural language question. The system is comprised of the NL2SQL model we developed, as well as the data simulation and the adaptive feedback framework to continuously improve model performance. The architecture of this NL2SQL model is built on our research on WikiSQL data, which we extended to support multitable scenarios via our unique table expand process. The data simulation and the feedback loop help the model continuously adjust to linguistic variation introduced by the domain specific knowledge.

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Author information

Authors and Affiliations

  1. Aunalytics, South Bend, IN, 46545, USA

    Kaiwen Dong, Kai Lu, Xin Xia, David Cieslak & Nitesh V. Chawla

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  1. Kaiwen Dong
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  2. Kai Lu
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  3. Xin Xia
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  4. David Cieslak
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  5. Nitesh V. Chawla
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Corresponding author

Correspondence to Kaiwen Dong .

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Editors and Affiliations

  1. Facebook AI, Seattle, WA, USA

    Yuxiao Dong

  2. Torre Telefonica, Barcelona, Spain

    Nicolas Kourtellis

  3. Bielefeld University, CITEC, Bielefeld, Germany

    Barbara Hammer

  4. Basque Center for Applied Mathematics, Bilbao, Spain

    Jose A. Lozano

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Dong, K., Lu, K., Xia, X., Cieslak, D., Chawla, N.V. (2021). An Optimized NL2SQL System for Enterprise Data Mart. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12979. Springer, Cham. https://doi.org/10.1007/978-3-030-86517-7_21

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  • DOI: https://doi.org/10.1007/978-3-030-86517-7_21

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