Earticle

초록

영어

This study is a comparative analysis of translated-text error-rates in song lyrics by K-Pop group New Jeans. Seven non-official machine translations (MTs) of ten songs were analyzed against official human-translated lyrics. The ten songs were consisted of a total 235 segments and the seven MTs were categorized under neural-network types (DeepL, Papago, Google Translate) and generative-AI types (ChatGPT, Bard, ClovaX, MS Bing Translate). Analysis discovered three salient points. First, neural-network types presented significantly higher error rates than generative-AI types. DeepL (66%), Papago (64%), Google Translate (59%). The most common errors were semantic and grammatical. A common feature of the errors was the poor contextual understanding and consistency between consecutive segments. This suggests that neural network MTs may have limited application for translating K-pop lyrics, which are expressive text. Second, neural network MTs were twice as erroneous as generative AI translations, with the official human translation as the baseline. T his suggests that AI translation may be more useful for translating K -pop lyrics than neural network MT in terms of semantic accuracy an d structural form. Third, generative AI translation quality improved in general when additional parameters and descriptions were provided via the services’ chat functions.

목차

1. 서론
2. 이론적 배경
2.1. 표현적 텍스트 번역
2.2. K-pop 가사 번역 관련 연구
2.3. 신경망 기계 번역
2.4. 생성형 AI 기계 번역
3. 분석자료 및 분석방법
3.1. 분석자료 선정
3.2. 분석방법
4. 분석결과
4.1. 신경망 기계 번역 분석결과
4.2. 생성형 AI 기계 번역 분석결과
4.3. 생성형 AI 기계 번역 프롬프트 활용 가능성
4.3.1. 부정확한 의미의 오류
4.3.2. 문법 오류
5. 결론
참고문헌

저자

• 황지연 [ Hwang, Ji-youn | 한국외국어대학교 ] 제1저자
• 이미령 [ Li, Mei-lian | 한국외국어대학교 ] 공동저자
• 원다인 [ Won, Da-in | 한국외국어대학교 ] 공동저자

참고문헌