Translating Cultural Words in News Articles Using DeepL: A Guide for Translators
Authors
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Sri Dewiyanti
Politeknik Negeri Bandung
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Mohammad Febryanto
Politeknik Negeri Bandung
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Ilma Yullinda Rahmah
Politeknik Negeri Bandung
https://orcid.org/0009-0007-3573-8282
DOI:
https://doi.org/10.36312/jolls.v6i3.4935Keywords:
Cultural words, News articles, DeepL translation, Translation strategiesAbstract
The key issue in machine-based news translation is the difficulty of translating culturally specific terms with local references into the target language without compromising clarity or cultural identity. Although DeepL Translator is known for producing high-quality translations, its ability to translate Indonesian cultural terms in news articles requires further evaluation. This study aims to analyze the translation strategies used by DeepL in translating Indonesian cultural terms and to evaluate the communicative adequacy of the resulting translations for international readers. This study employs a qualitative approach using an embedded case study design. The data consisted of 117 cultural terms obtained from 25 news articles published by the Pikiran Rakyat Media Network. The analysis was conducted based on Newmark’s categories of culture and translation strategies, and the translations were evaluated in terms of accuracy, clarity, and cultural appropriateness. The findings indicate that the material culture is the most dominant type of cultural term (60 instances), while the strategy most frequently used is transference (55 instances). This strategy successfully preserves the basic meaning and readability but often fails to convey cultural context, metaphorical nuances, and socio-religious meanings, potentially leading to misunderstandings among international readers. Therefore, this study recommends human post-editing through the couplet strategy, which combines transference with descriptive or functional explanations, to enhance communicative clarity without sacrificing the authenticity of meaning. These findings highlight the need for human-machine collaboration in news translation and provide practical insights for translators, editors, and media practitioners, particularly when dealing with culturally sensitive content.
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