Utilizing a Cultural Translation Evaluation Framework for Semantic Drift in AI-Based Multilingual Translation

• 발행기관: 한국통번역교육학회
• 저자명: Mi-ae Yang Jung-ho Chang
• 간행물 정보: 『통번역교육연구』제23권 제1호, 53~81쪽, 전체 29쪽
• 주제분류: 어문학 > 언어학
• 파일형태: PDF
• 발행일자: 2025.06.30

국문 초록

This study investigates how three state-of-the-art AI translation models—GPT-4, DeepL, and NLLB-200—handle semantic drift, cultural fidelity, and affective fidelity in the translation of 100 culturally embedded Korean-language sentences. Semantic drift, defined as the diachronic or contextual evolution of word meanings, serves as a critical lens through which the preservation of cultural nuance in translation is assessed. Experimental findings reveal that GPT-4 demonstrates the highest level of performance in both contextual interpretation and emotional expressiveness, effectively preserving the cultural logic of the source text. DeepL excels in grammatical precision but falls short in cultural interpretation. NLLB-200, while offering broad multilingual coverage, exhibits limited cultural sensitivity and fails to adequately capture symbolic meaning. Based on these results, the study proposes a complementary qualitative evaluation framework that addresses the limitations of conventional quantitative metrics such as BLEU. It further outlines a future direction for AI translation systems that prioritizes not only linguistic accuracy but also cultural receptivity and ethical interpretability in multilingual,

목차

Ⅰ. Introduction

Ⅱ. Theoretical Framework

Ⅲ. Methodology

Ⅳ. Results and Analysis

Ⅴ. Conclusion and Recommendations

References

키워드

AI 기반 번역 모델 의미 변형 다국어 번역 번역 품질 평가 프레임워크 문화적 맥락 번역 AI-Based Translation Models Semantic Drift Multilingual Translation Translation Evaluation Framework Cultural Translation