earticle

영어

This study examines the performance of four major AI translation tools Google Translate, Papago, DeepL, and ChatGPT in translating poetry between Korean and English in both directions. Poetry translation presents unique challenges, including cultural context, stylistic intricacies, and emotional resonance, which machine translation tools traditionally struggle to capture. The research utilizes Juliane House’s 2015 Translation Quality Assessment model(the TQA), combined with a custom numerical rubric, to evaluate both covert and overt errors in translation. To ensure cultural and poetic variety, the study analyzes two canonical poems: Seo, Jeong-ju’s “Beside the Chrysanthemums” (Korean) and Robert Frost’s “The Road Not Taken” (English). These serve as case studies for both Korean-to-English (K2E) and English-to-Korean (E2K) translation tasks. Evaluation scores from both human and AI assessors reveal that ChatGPT achieved the highest performance, scoring 93/100 on K2E, closely rivaling human translations, while DeepL scored significantly lower at 72/100. Results also indicate that translations from English to Korean (E2K) were consistently more challenging, with average scores dropping by 7.75 points across all tools. Strong correlations between AI and human evaluation scores (r 0.83) suggest that AI models, particularly ChatGPT, can support high-quality literary translation assessments. The study highlights the emerging role of generative AI as an assistant not a replacement in poetry translation workflows, suggesting a promising future for AI-human collaboration in preserving literary nuance and emotional depth.