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다국어 BERT를 활용한 한국어 자연어 질의의 SQL 변환Text-to-SQL for Korean Language based on Multilingual BERT

Other Titles
Text-to-SQL for Korean Language based on Multilingual BERT
Authors
윤훈상허재혁김정섭강필성
Issue Date
2022
Publisher
대한산업공학회
Keywords
Text-to-SQL; Multilingual BERT; WikiSQL; SQLova; HydraNet; Bridge; Back Translation
Citation
대한산업공학회지, v.48, no.1, pp.91 - 104
Indexed
KCI
Journal Title
대한산업공학회지
Volume
48
Number
1
Start Page
91
End Page
104
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/142064
ISSN
1225-0988
Abstract
Text-to-SQL is one of semantic parsing methods that converts natural language questions into SQL queries, and it aims to extract data from any relational database without knowledge of SQL query configuration. Although development of large amounts of datasets (WikiSQL, SPIDER) and development of pre-trained language models (BERT) contributed to the improvement of Text-to-SQL performance in English, language-specific dataset construction and model research have not been much progressed. Therefore, this study proposes a multilingual BERT-based Text-to-SQL methodology that converts the natural language question in Korean into SQL query for an English database. To this end, four strategies for translating Korean queries into English were explored, and their effectiveness was verified by applying each strategy to three text-to-SQL model structures. As a result of the experiment, it was confirmed that it showed a significant SQL generation performance even for Korean questions. The proposed methodology is meaningful in that it shows semantic inferences between database tables, column information, and questions composed of different languages are possible, and it is expected to support efficient database access by Korean users who lack proficiency in writing SQL queries.
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공과대학 (산업경영공학부)
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