Research on AI and Human Translation of Longshan Black Pottery Culture-loaded Terms: A Case Study of DeepL
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Keywords
Longshan Black Pottery culture, culture-loaded terms, DeepL, AI translation, human translation
Abstract
As digital technologies advance, artificial intelligence (AI) translation has increasingly been applied to the cross-linguistic dissemination of intangible cultural heritage (ICH). However, when handling culture-loaded terms with profound cultural connotations, AI translation remains prone to semantic deviations and loss of cultural nuance. This study focuses on the culture-loaded terms of Longshan Black Pottery, drawing on a comparative corpus of DeepL and human translations. Drawing on contrastive analysis and case study methods, this study operates within the theoretical framework of ecological translation studies’ three-dimensional transformation, examining translations from three perspectives: semantic accuracy, cultural conveyance, and communicative acceptability. The findings indicate that human translation outperforms AI in conveying cultural connotations and adapting to contextual nuances, while AI translation, although efficient, remains limited in cultural reproduction and contextual understanding. Based on these findings, the study proposes translation optimization strategies-including transliteration compensation, imagery transformation, and explicitation-and puts forward a human-AI collaborative translation model. The results offer empirical insights for improving the English translation of ICH culture-loaded terms and for the optimization of AI translation tools.
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