Earticle

초록

영어

Neural Machine Translation (NMT) is known to produce better translation than Statistic Machine Translation(SMT). It can be natural that scholars and media show more interest in the emerging translation technology. However, it is difficult to define what makes a translation better than others. Furthermore, the claims about the NMT quality and wild guess about the future of translation are sometimes misleading and even worrisome. This means more objective and reliable diagnosis about the current state of NMT is necessary so that potential users and readers of machine translation can make informed decisions. Hence, this research aims to investigate if the media's approach, mainly driven by automatic evaluation, is valid and to review what has been considered as important aspects for translation evaluation in the previous translation studies. As a next step toward a more objective diagnosis, this research tries to compare three Neural Machine Translation (NMT) services available in Korea and assess their translation quality from various perspectives. In total, eight translation experts conducted a series of quality assessment on Korean to English and English and Korean translation. The analysis of the experts input will provide a few meaningful findings that can help build more objective yet prudent views on neural machine translation.

목차

1. 서론
2. 현황 및 선행연구
2.1. 현황
2.2. 선행연구
3. 연구 설계
3.1. 텍스트 및 엔진의 선정
3.2. 평가자의 선정 및 평가 방법
4. 분석 결과
4.1. 오류의 정량화
4.2. 기계번역 상대평가
4.3. 기계번역 평가에 영향을 준 요소
4.4. 기계번역의 명확성
4.5. 기계번역의 유창성
4.6. 기계번역의 실무적용 가능성과 포스트에디팅
5. 논의 및 결론
참고문헌

키워드

기계번역 번역품질 신경망번역 구글번역 파파고 카카오i machine translation translation quality NMT Google translation Papago Kakaoi

저자

• 이준호 [ Lee Jun-ho | 서울외국어대학원대학교 ]

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