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A study comparing human and AI translations of literary works shows that while machine translations are deemed 'fine', readers still prefer human translations for their immersiveness and clarity. Automatic metrics fail to capture reader preferences.
This paper investigates whether LLM translations exhibit identifiable emotional profiles and how post-editing reshapes them toward human-like norms, using a comparative study of Margaret Atwood's 'Oryx and Crake' translated to Italian.
This paper empirically examines the tradeoff between fluency and faithfulness in literary translation using 130,486 paragraphs from 106 novels, finding a consistent negative correlation for human and Google Translate translations, but weaker for TranslateGemma.
This academic study on arXiv examines ChatGPT-4's performance in translating literary prose between Arabic and English, involving 30 professional translators who evaluated and postedited AI-generated translations. The research finds that while AI improves translation speed, human postediting remains essential for handling cultural, stylistic, and figurative language aspects, suggesting a human-machine collaboration model rather than full automation.