How well can state-of-the-art machine translation systems render a 16th-century Chinese novel?
Mu You, Derek F. Wong, Jing Zhang, Kaixin Lan · Cadernos de Tradução · 2025
This study evaluates the performance of state-of-the-art machine translation systems in rendering Journey to the West, a culturally rich 16th-century Chinese novel, into Portuguese. Employing a mixed-methods approach, we compare translations produced by DeepSeek-V3, GPT-4o, DeepL Pro, and NovelTrans-J against a published human translation. Quantitative assessments conducted by an expert evaluator examine accuracy, fluency, stylistic elegance, cultural appropriateness, and overall translation quality at both the sentence and chunk levels. The results reveal that three MT systems (DeepSeek-V3, GPT-4o, and NovelTrans-J) produce translations of comparable or superior quality to the human translation. Among them, NovelTrans-J consistently outperforms all other participants, particularly in terms of cultural appropriateness. In contrast, DeepL Pro demonstrates significantly weaker performance across all evaluated dimensions. To complement the quantitative analysis, a qualitative investigation focuses on the rendering of culture-specific items (CSIs). NovelTrans-J exhibits outstanding performance, producing the fewest mistranslations and uniquely providing explanatory notes that facilitate reader comprehension. DeepSeek-V3 and GPT-4o also handle CSIs competently, though with less consistency, while DeepL Pro struggles considerably, showing a high rate of CSI mistranslations and generally low quality. Interestingly, the human translation also contains notable CSI-related errors, particularly in cases involving semantically opaque expressions, an area in which all participants encounter significant difficulty. These findings underscore the growing potential of MT systems to handle complex, culturally rich literary texts, although certain challenges, such as the translation of semantically opaque expressions, remain significant obstacles. We hope this study provides an updated perspective on the current capabilities of MT and offers practical insights to guide the development of future systems that can more accurately capture and transmit the distinctive cultural nuances embedded in literary works.