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
- 2021.8
- 수록면
- 141 - 162 (22page)
- DOI
- 10.22844/its.2021.25.3.141
이용수
초록· 키워드
This paper begins with the question. Is it possible that a machine can understand and evaluate the meaning of a text? Word2Vec is a neural network method of computing vector representations of words, which means the tool processes a text corpus and produces numeric vectors on the meanings of the words. This paper seeks to examine whether the automatic evaluation metrics, BLEUmodif and METEORmodif, enhanced by word2vec technology, are able to capture the meaning of translated texts better than their standard versions. To this end, first, five literary texts written in English were translated into Korean by 119 students. Then, these translations were evaluated by two professional translators/teachers and by five different automatic evaluation programs. These are standard versions of BLEU and METEOR as well as BLEUmodif and METEORmodif with the word2vec technology and finally Sent2Vec that computes sentence vector representations. For the sake of convenience, the last three systems are referred to as “embedding versions” in this study. The analysis shows that overall, the standard version of METEOR performed the best (grades: 0.838, ranking: 0.727), but at the level of individual texts, the embedding versions of BLEU and METEOR showed a higher correlation with human evaluation (four texts) than their standard versions (one text).
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
목차
등록된 정보가 없습니다.
참고문헌
참고문헌 신청최근 본 자료
