研究文学翻译中机器对隐喻的处理问题，发现三分之一被人工修改。
Metaphors in Literary Post-Editing: Opening Pandora's Box?
- 分析译后编辑者如何修正神经网络与大模型翻译的隐喻表达
- 三分之一隐喻被修改，整体翻译质量被评为较差
- 适合关注文学翻译自动化与译者创造力的读者
本文研究文学文本译后编辑者对神经机器翻译（NMT）和大语言模型（LLMs）翻译隐喻的反应。结果显示，输出中每三个隐喻就有一个被编辑者修改，表明文学机器翻译在处理修辞语言方面确实存在严重问题。编辑者意识到过度字面化翻译的存在，尤其在多词表达中更为明显。部分情况下，他们难以判断修改方案是否合理。整体评价认为机器翻译质量偏低，译后编辑耗时耗力，甚至超过从头翻译。这支持了先前研究观点：译后编辑限制译者创造力，并削弱其对文本的归属感。
原文摘要 · Abstract (English)
This paper investigates how post-editors of literary texts react and respond to the way metaphors have been translated by Neu ral Machine Translation (NMT) and Large Language Models (LLMs). The results show that one in three metaphors in the output were changed by the post-editors, demonstrating that the translation of fig urative language is indeed problematic in literary MT (LitMT). The responses indi cate that the post-editors were aware of overly literal translations, though mostly for multiword expressions. Moreover, at times they found it difficult to determine whether solutions were acceptable. They rated the overall quality of the MT out put as quite poor and stated that the post editing was more work and more effort than it would have been translating from scratch. This supports previous studies ar guing that post-editing constrains transla tors in their creativity and diminishes their sense of text ownership.
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