A Corpus based Comparison of Human and Machine News Translation Styles
A case study of translations of Ministry of Foreign Affairs news
DOI:
https://doi.org/10.54097/7611ff88Keywords:
Large Language Models, Corpus, Translator StyleAbstract
This study compares the styles of news translations produced by human translators, neural machine translation system, and large language model. Using a corpus-based approach, it examines lexical, syntactic, and discourse features across the three sets of translations. Type–token ratios, mean word length, mean sentence length, and readability scores reveal no clear stylistic differences. The findings suggest that advances in machine translation have made human and machine translations difficult to distinguish on stylistic grounds in this news corpus.
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