An Error Analysis of DeepL Translation in Children’s Literature: A Case Study of Dolly and Her Little Red Umbrella
Abstract
This study aims to examine how effective is the DeepL Language Translator when translating the story Dolly and Her Little Red Umbrella from English into Indonesian language. Error analysis was conducted on the translation using the taxonomy of machine translation errors by Costa et al. (2015). A total of 36 errors were detected during the analysis and these errors were classified into four categories namely semantic, syntactic, lexical and pragmatic errors with the frequency of occurrence of 5, 1, 5 and 23 respectively. The most common type of error was pragmatic, highlighting challenges in conveying the cultural and contextual aspects of language in children’s literature. These issues often stem from DeepL's difficulty in capturing subtle meanings, idioms, and unspoken cues that are crucial for accurate translation. The findings suggest that while DeepL demonstrates strong grammatical accuracy, improvements are needed in understanding and conveying contextual subtleties and cultural references. To enhance translation quality, future advancements should focus on refining context comprehension, incorporating feedback from language experts, and leveraging sophisticated machine learning techniques.
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