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Software Technology Research
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 9
2800-2804

Multi-task learning method for NL2SQL with fused table columns

Liu Yanga
Liao Weia
Xu Zhenb
a. School of Electronic & Electrical Engineering, b. School of Mechanical & Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
DOI: 10.19734/j.issn.1001-3695.2023.12.0629

Abstract

Existing NL2SQL approaches do not fully utilize the information of data table columns, which plays an important role in the semantic understanding of the problem and the logical generation of SQL statements. This paper proposed an NL2SQL method that fused data table columns(FC-SQL) to improve the overall accuracy of SQL generation. Firstly, this method utilized BERT to merge the problem and database table columns for encoded representations. Secondly, it used multi-task learning approach to construct a multi-task network by combining parallel and cascade to predict different sub-tasks. Finally, for the conditional value extraction sub-task, this method computed the similarity between the words in the problem and the table columns by fusing the information of the columns, and it used the similarity value as a weight to compute each word as the conditional value probability of each word as a conditional value, thus improving the accuracy of conditional value prediction. The logical form accuracy and SQL execution accuracy on the TableQA dataset reach 88.23% and 91.65%, respectively. This paper designed ablation experiments to verify the effect of table columns information on the model. The experimental results show that the incorporation of table columns improves the effectiveness of the conditional value extraction sub-task, which in turn improves the overall accuracy of the NL2SQL task and provides better SQL generation compared to the comparison model.

Foundation Support

国家自然科学基金资助项目(62001282)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.12.0629
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 9
Section: Software Technology Research
Pages: 2800-2804
Serial Number: 1001-3695(2024)09-033-2800-05

Publish History

[2024-05-10] Accepted Paper
[2024-09-05] Printed Article

Cite This Article

刘洋, 廖薇, 徐震. 融合表字段的NL2SQL多任务学习方法 [J]. 计算机应用研究, 2024, 41 (9): 2800-2804. (Liu Yang, Liao Wei, Xu Zhen. Multi-task learning method for NL2SQL with fused table columns [J]. Application Research of Computers, 2024, 41 (9): 2800-2804. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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