Creativity in translation: machine translation as a constraint for literary texts
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Abstract (arXiv)
This article presents the results of a study involving the translation of a short story by Kurt Vonnegut from English to Catalan and Dutch using three modalities: machine-translation (MT), post-editing (PE) and translation without aid (HT). Our aim is to explore creativity, understood to involve novelty and acceptability, from a quantitative perspective. The results show that HT has the highest creativity score, followed by PE, and lastly, MT, and this is unanimous from all reviewers. A neural MT system trained on literary data does not currently have the necessary capabilities for a creative translation; it renders literal solutions to translation problems. More importantly, using MT to post-edit raw output constrains the creativity of translators, resulting in a poorer translation often not fit for publication, according to experts.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 3 of its 52 references that are in the map. First posted on arXiv 12 Apr 2022.
Builds on
3 of 52 references.
References · 3
- 2017Attention Is All You Needcited by 59,932
- 2021The Impact of Post-editing and Machine Translation on Creativity and Reading Experiencecited by 10
- 2020Machine Translation of Novels in the Age of Transformercited by 4
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 3; 2026: 4.
10 papers.
- 2024How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMscited by 23
- 2025Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editingcited by 3
- 2022Multi-Figurative Language Generationcited by 2
- 2025To MT or not to MT: An eye-tracking study on the reception by Dutch readers of different translation and creativity levelscited by 2
- 2023To be or not to be: a translation reception study of a literary text translated into Dutch and Catalan using machine translationcited by 1
- 2026Creativity Bias: How Machine Evaluation Struggles with Creativity in Literary Translationscited by 1
- 2025Missing the human touch? A computational stylometry analysis of GPT-4 translations of online Chinese literaturecited by 0
- 2026AI Technologies in Language Access: Attitudes Towards AI and the Human Value of Language Access Managerscited by 0
- 2026Metaphors in Literary Post-Editing: Opening Pandora's Box?cited by 0
- 2026Better Literary Translation: A Multi-Aspect Data Generation and LLM Training Approachcited by 0
- 2026Better Literary Translation: A Multi-Aspect Data Generation and LLM Training Approach4 Jun 2026
- 2026Metaphors in Literary Post-Editing: Opening Pandora's Box?20 May 2026
- 2026AI Technologies in Language Access: Attitudes Towards AI and the Human Value of Language Access Managers19 May 2026
- 2026Creativity Bias: How Machine Evaluation Struggles with Creativity in Literary Translations13 May 2026
- 2025Missing the human touch? A computational stylometry analysis of GPT-4 translations of online Chinese literature16 Jun 2025
- 2025To MT or not to MT: An eye-tracking study on the reception by Dutch readers of different translation and creativity levels28 Apr 2025
- 2025Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editing3 Apr 2025
- 2024How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMs24 Oct 2024
- 2023To be or not to be: a translation reception study of a literary text translated into Dutch and Catalan using machine translation5 Jul 2023
- 2022Multi-Figurative Language Generation5 Sep 2022