인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.02
- 수록면
- 91 - 104 (14page)
- DOI
- 10.7232/JKIIE.2022.48.1.091
이용수
초록· 키워드
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.
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
목차
- 1. 서론
- 2. 대표적 Text-to-SQL 데이터셋 및 모델
- 3. 한국어 WikiSQL 데이터셋 구축
- 4. Experiments
- 5. 결론
- 참고문헌
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-ECN-0101-2022-530-000196211
