감성 에세이 번역에서 생성형 AI의 문체 재현 양상 연구 : 한일 번역 사례 기반 정량·정성 분석
Stylistic Reproduction in Generative AI Translation of Emotional Essays : A Quantitative and Qualitative Analysis of Korean-Japanese Translation Cases
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
This study investigates how effectively generative AI models reproduce the stylistic features of Korean emotional essays in Japanese translation. Using I Decided to Live as Myself, we compared human translation with three AI-generated translations. Semantic similarity was measured with BERTScore, and stylistic variation was assessed through sentence-level embedding distances based on SentenceTransformer. The quantitative results show stable semantic fidelity across AI models but considerable divergence in stylistic embeddings, indicating stylistic variation despite preserved meaning. Qualitative analysis further revealed differences in sentence structuring, emotional tone, cultural contextualization, and information organization between human and AI translations. The findings demonstrate that meaning and style can diverge substantially in emotional-text translation and that AI models reproduce stylistic cues only partially. This study provides foundational insight for developing stylistic evaluation metrics and advancing research on AI translation of emotion-centered texts.
Abstract
This study investigates how effectively generative AI models reproduce the stylistic features of Korean emotional essays in Japanese translation. Using I Decided to Live as Myself, we compared human translation with three AI-generated translations. Semantic similarity was measured with BERTScore, and stylistic variation was assessed through sentence-level embedding distances based on SentenceTransformer. The quantitative results show stable semantic fidelity across AI models but considerable divergence in stylistic embeddings, indicating stylistic variation despite preserved meaning. Qualitative analysis further revealed differences in sentence structuring, emotional tone, cultural contextualization, and information organization between human and AI translations. The findings demonstrate that meaning and style can diverge substantially in emotional-text translation and that AI models reproduce stylistic cues only partially. This study provides foundational insight for developing stylistic evaluation metrics and advancing research on AI translation of emotion-centered texts.