Literary post-editors struggle with NMT and LLM metaphor translation

A new paper explores how human post-editors handle metaphors translated by Neural Machine Translation and Large Language Models in literary texts. The study found that post-editors frequently altered metaphors, rating the machine translation output as poor and the post-editing process as more demanding than translating from scratch. These findings suggest that current NMT and LLM approaches struggle with figurative language in literary contexts, potentially limiting translator creativity and ownership. AI

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IMPACT Reveals significant challenges for LLMs and NMT in translating nuanced figurative language, potentially impacting literary translation workflows.

RANK_REASON The cluster contains an academic paper discussing the challenges of metaphor translation by NMT and LLMs in literary contexts. [lever_c_demoted from research: ic=1 ai=1.0]

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• Large Language Models
• Neural Machine Translation

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