AI 번역과 인간 번역의 은유 표현 번역 결과 분석 – 뉴마크의 번역 방법을 중심으로 -
An Analysis of AI and Human Translation Outputs of Metaphor Translation Focusing on Newmark’s Translation Methods
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This study aims to evaluate the quality of AI translation in comparison to human translation, identify challenges in applyingmetaphor translation methods to AI-generated texts, and examine the relationship between translation quality and the strategies employed. The analysis delves into the Korean translation of metaphorical expressions from the Chinese literary work Dawn Blossoms Plucked at Dusk, utilizing outputs from Google Translate and ChatGPT. The initial step of this study involved the categorization and statistical analysis of translation errors generated by AI. The findings show that literal translations and omissions were relatively infrequent, whereas distortions of meaning and stylistic issues were more prevalent. Furthermore, this study revealed that the types and frequency of errors were reduced when AI translation employed the same methods as human translation. It is anticipated that the findings of this study will serve as foundational data for future research. However, the study is constrained by limited data and a lack of diversity, which calls for further investigation to overcome these limitations.
Abstract
This study aims to evaluate the quality of AI translation in comparison to human translation, identify challenges in applyingmetaphor translation methods to AI-generated texts, and examine the relationship between translation quality and the strategies employed. The analysis delves into the Korean translation of metaphorical expressions from the Chinese literary work Dawn Blossoms Plucked at Dusk, utilizing outputs from Google Translate and ChatGPT. The initial step of this study involved the categorization and statistical analysis of translation errors generated by AI. The findings show that literal translations and omissions were relatively infrequent, whereas distortions of meaning and stylistic issues were more prevalent. Furthermore, this study revealed that the types and frequency of errors were reduced when AI translation employed the same methods as human translation. It is anticipated that the findings of this study will serve as foundational data for future research. However, the study is constrained by limited data and a lack of diversity, which calls for further investigation to overcome these limitations.