Metaphors in Literary Post-Editing: Opening Pandora's Box?

TL;DR

This study examines how literary post-editors respond to metaphor translations by NMT and LLMs, revealing challenges in translating figurative language and its impact on editing effort and translator creativity.

Contribution

It provides empirical insights into post-editors' reactions to metaphor translation issues in literary machine translation, highlighting the complexity of figurative language handling.

Findings

One in three metaphors were altered by post-editors.

Post-editors found literal translations problematic, especially for multiword expressions.

Overall quality of MT output was rated as poor, increasing editing effort.

Abstract

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.