Exploring Semantic Equivalence in the Translation of the Putri Hijau Legend

This study investigates the capabilities of neural machine translation (NMT) tools, Google Translate and DeepL, in translating culturally rich texts, with a focus on the Putri Hijau legend. Using Newmark’s Semantic Translation Theory, the research evaluates the translation of idiomatic expressions, metaphors, and culturally embedded terms to determine the semantic fidelity and cultural appropriateness of each tool. The findings reveal that DeepL outperforms Google Translate in preserving contextual and pragmatic nuances, particularly in idiomatic expressions and culturally specific terms, aligning more closely with semantic translation principles. However, both tools face significant challenges in translating metaphors, often resorting to literal interpretations that fail to capture the symbolic and poetic elements of the original text. The study highlights the limitations of current NMT tools in rendering complex cultural and figurative content accurately and underscores the importance of cultural understanding in translation. While DeepL demonstrates greater effectiveness, the research concludes that NMT tools should be used as supplementary aids rather than standalone solutions for culturally significant texts. Recommendations include further refinement of AI models to enhance their ability to handle figurative language and cultural nuances. These findings provide valuable insights for improving NMT technology and its application in translating culturally sensitive materials.