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

arXiv: https://arxiv.org/abs/2605.21178 · HTML: https://mlsanity.com/paper/2605.21178/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 53 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 11 of 11)

- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Achieving Human Parity on Automatic Chinese to English News Translation](https://mlsanity.com/paper/1803.05567/) (2018)
- [Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation](https://mlsanity.com/paper/1808.07048/) (2018)
- [Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature](https://mlsanity.com/paper/2210.14250/) (2022)
- [Neural versus Phrase-Based Machine Translation Quality: a Case Study](https://mlsanity.com/paper/1608.04631/) (2016)
- [What Level of Quality can Neural Machine Translation Attain on Literary Text?](https://mlsanity.com/paper/1801.04962/) (2018)
- [The Impact of Post-editing and Machine Translation on Creativity and Reading Experience](https://mlsanity.com/paper/2101.06125/) (2021)
- [Creativity in translation: machine translation as a constraint for literary texts](https://mlsanity.com/paper/2204.05655/) (2022)
- [Towards Tailored Recovery of Lexical Diversity in Literary Machine Translation](https://mlsanity.com/paper/2408.17308/) (2024)
- [To be or not to be: a translation reception study of a literary text translated into Dutch and Catalan using machine translation](https://mlsanity.com/paper/2307.02358/) (2023)

## Abstract (arXiv)

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.