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ACL-SQL: Generating SQL Queries from Natural Language

Published: 02 January 2021 Publication History

Abstract

Relational databases are prevalent in handling large and complex databases; however, writing the SQL queries can be a tedious task for databases. Recent studies for automating the task of translating natural language text to SQL query are not robust to queries having multi-table dependencies. Also, considering the lack of such research in scientific domain, we introduce ACL-SQL, a dataset with complex queries depending on up to five tables and benchmark the dataset on a simple approach. Evaluation shows that our approach is reasonably precise and can be adopted for practical applications. The dataset and codes are available at https://github.com/rohitshantarampatil/sql-nlp.

References

[1]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. (2018). arxiv:cs.CL/1810.04805
[2]
Monarch Parmar, Naman Jain, Pranjali Jain, P Jayakrishna Sahit, Soham Pachpande, Shruti Singh, and Mayank Singh. 2020. NLPExplorer: exploring the universe of NLP papers. In European Conference on Information Retrieval. Springer, 476–480.
[3]
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev. 2018. Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task. (2018). arxiv:cs.CL/1809.08887

Cited By

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  • (2022)Querylizer: An Interactive Platform for Database Design and Text to SQL Conversion2022 International Conference for Advancement in Technology (ICONAT)10.1109/ICONAT53423.2022.9725828(1-6)Online publication date: 21-Jan-2022
  • (2021)Natural Language Query to SQL Conversion Using Machine Learning Approach2021 3rd International Conference on Sustainable Technologies for Industry 4.0 (STI)10.1109/STI53101.2021.9732586(1-6)Online publication date: 18-Dec-2021
  • (2021)Metaknowledge Extraction Based on Multi-Modal DocumentsIEEE Access10.1109/ACCESS.2021.30687289(50050-50060)Online publication date: 2021

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cover image ACM Other conferences
CODS-COMAD '21: Proceedings of the 3rd ACM India Joint International Conference on Data Science & Management of Data (8th ACM IKDD CODS & 26th COMAD)
January 2021
453 pages
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 January 2021

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  • Extended-abstract
  • Research
  • Refereed limited

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CODS COMAD 2021
CODS COMAD 2021: 8th ACM IKDD CODS and 26th COMAD
January 2 - 4, 2021
Bangalore, India

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Overall Acceptance Rate 197 of 680 submissions, 29%

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Cited By

View all
  • (2022)Querylizer: An Interactive Platform for Database Design and Text to SQL Conversion2022 International Conference for Advancement in Technology (ICONAT)10.1109/ICONAT53423.2022.9725828(1-6)Online publication date: 21-Jan-2022
  • (2021)Natural Language Query to SQL Conversion Using Machine Learning Approach2021 3rd International Conference on Sustainable Technologies for Industry 4.0 (STI)10.1109/STI53101.2021.9732586(1-6)Online publication date: 18-Dec-2021
  • (2021)Metaknowledge Extraction Based on Multi-Modal DocumentsIEEE Access10.1109/ACCESS.2021.30687289(50050-50060)Online publication date: 2021

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