Translating Natural Language Queries to SQL Using the T5 Model

被引：0

作者：

Wong, Albert ^{[1
]}

Pham, Lien ^{[1
]}

Lee, Young ^{[2
]}

Chan, Shek ^{[1
]}

Sadaya, Razel ^{[1
]}

Khmelevsky, Youry ^{[3
]}

Clement, Mathias ^{[3
]}

Cheng, Florence Wing Yau ^{[1
]}

Mahony, Joe ^{[4
]}

Ferri, Michael ^{[4
]}

机构：

[1] Langara Coll, Math & Stat, Vancouver, BC, Canada

[2] Okanagan Coll, Math & Stat, Kelowna, BC, Canada

[3] Okanagan Coll, Comp Sci, Kelowna, BC, Canada

[4] Harris SmartWorks, Res & Dev, Ottawa, ON, Canada

来源：

18TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE, SYSCON 2024 | 2024年

基金：

加拿大自然科学与工程研究理事会;

关键词：

Natural Language Processing; Data Query System; Text-to-SQL; Speech-to-SQL; Deep Learning; Machine Learning; T5; Model; Human-Machine-Systems; Energy Systems;

D O I：

10.1109/SysCon61195.2024.10553509

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper presents the development process of a natural language to SQL model using the T5 model as the basis. The models, developed in August 2022 for an online transaction processing system and a data warehouse, have a 73% and 84% exact match accuracy respectively. These models, in conjunction with other work completed in the research project, were implemented for several companies and used successfully on a daily basis. The approach used in the model development could be implemented in a similar fashion for other database environments and with a more powerful pre-trained language model.

引用

页数：7

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