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Abstract
The aim of this paper is to propose a framework to support literary translators—particularly poetry translators—in making effective use of large language models (LLMs) through established prompt engineering strategies applied to both pre-translation and translation stages. The paper illustrates these strategies using poems characterized by multiple layers of syntactic, semantic, phonological, and cultural complexity, and discusses how LLMs perform in response to each prompting technique. It also engages with the longstanding claim that poetry translation is a purely human endeavour and cannot be computer-assisted, arguing instead that LLMs, rather than replacing human creativity, have the potential to extend it.- Anthology ID:
- 2026.eamt-1.50
- Volume:
- Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)
- Month:
- June
- Year:
- 2026
- Address:
- Tilburg, The Netherlands
- Editors:
- Dimitar Shterionov, Eva Vanmassenhove, Mirella De Sisto, Fred Blain, Javad Pourmostafa Roshan Sharami, Lisa Lepp, Chiara Manna, Argentina Anna Rescigno, Alina Karakanta, Ayla Rigouts Terryn, Manuel Lardelli, Natalia Resende, Elena Murgolo, Janiça Hackenbuchner, Anna Zaretskaya, Miquel Esplà-Gomis, Thierry Etchegoyhen, Dagmar Gromann, Rachel Bawden, Barry Haddow, Sara Szoc, Mikel Forcada, Helena Moniz
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 787–799
- Language:
- URL:
- https://aclanthology.org/2026.eamt-1.50/
- DOI:
- Bibkey:
- Cite (ACL):
- Natalia Resende and James Hadley. 2026. Extending Creativity: Large Language Models and the Practice of Poetry Translation. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 787–799, Tilburg, The Netherlands. European Association for Machine Translation.
- Cite (Informal):
- Extending Creativity: Large Language Models and the Practice of Poetry Translation (Resende & Hadley, EAMT 2026)
- Copy Citation:
- PDF:
- https://aclanthology.org/2026.eamt-1.50.pdf
Export citation
@inproceedings{resende-hadley-2026-extending,
title = "Extending Creativity: Large Language Models and the Practice of Poetry Translation",
author = "Resende, Natalia and
Hadley, James",
editor = "Shterionov, Dimitar and
Vanmassenhove, Eva and
De Sisto, Mirella and
Blain, Fred and
Pourmostafa Roshan Sharami, Javad and
Lepp, Lisa and
Manna, Chiara and
Rescigno, Argentina Anna and
Karakanta, Alina and
Rigouts Terryn, Ayla and
Lardelli, Manuel and
Resende, Natalia and
Murgolo, Elena and
Hackenbuchner, Jani{\c{c}}a and
Zaretskaya, Anna and
Espl{\`a}-Gomis, Miquel and
Etchegoyhen, Thierry and
Gromann, Dagmar and
Bawden, Rachel and
Haddow, Barry and
Szoc, Sara and
Forcada, Mikel and
Moniz, Helena",
booktitle = "Proceedings of the 26th Annual Conference of the {E}uropean Association for Machine Translation (Volume 1)",
month = jun,
year = "2026",
address = "Tilburg, The Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.eamt-1.50/",
pages = "787--799",
ISBN = "9789403901411",
abstract = "The aim of this paper is to propose a framework to support literary translators{---}particularly poetry translators{---}in making effective use of large language models (LLMs) through established prompt engineering strategies applied to both pre-translation and translation stages. The paper illustrates these strategies using poems characterized by multiple layers of syntactic, semantic, phonological, and cultural complexity, and discusses how LLMs perform in response to each prompting technique. It also engages with the longstanding claim that poetry translation is a purely human endeavour and cannot be computer-assisted, arguing instead that LLMs, rather than replacing human creativity, have the potential to extend it."
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%0 Conference Proceedings %T Extending Creativity: Large Language Models and the Practice of Poetry Translation %A Resende, Natalia %A Hadley, James %Y Shterionov, Dimitar %Y Vanmassenhove, Eva %Y De Sisto, Mirella %Y Blain, Fred %Y Pourmostafa Roshan Sharami, Javad %Y Lepp, Lisa %Y Manna, Chiara %Y Rescigno, Argentina Anna %Y Karakanta, Alina %Y Rigouts Terryn, Ayla %Y Lardelli, Manuel %Y Resende, Natalia %Y Murgolo, Elena %Y Hackenbuchner, Janiça %Y Zaretskaya, Anna %Y Esplà-Gomis, Miquel %Y Etchegoyhen, Thierry %Y Gromann, Dagmar %Y Bawden, Rachel %Y Haddow, Barry %Y Szoc, Sara %Y Forcada, Mikel %Y Moniz, Helena %S Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1) %D 2026 %8 June %I European Association for Machine Translation %C Tilburg, The Netherlands %@ 9789403901411 %F resende-hadley-2026-extending %X The aim of this paper is to propose a framework to support literary translators—particularly poetry translators—in making effective use of large language models (LLMs) through established prompt engineering strategies applied to both pre-translation and translation stages. The paper illustrates these strategies using poems characterized by multiple layers of syntactic, semantic, phonological, and cultural complexity, and discusses how LLMs perform in response to each prompting technique. It also engages with the longstanding claim that poetry translation is a purely human endeavour and cannot be computer-assisted, arguing instead that LLMs, rather than replacing human creativity, have the potential to extend it. %U https://aclanthology.org/2026.eamt-1.50/ %P 787-799
Markdown (Informal)
[Extending Creativity: Large Language Models and the Practice of Poetry Translation](https://aclanthology.org/2026.eamt-1.50/) (Resende & Hadley, EAMT 2026)
- Extending Creativity: Large Language Models and the Practice of Poetry Translation (Resende & Hadley, EAMT 2026)
ACL
- Natalia Resende and James Hadley. 2026. Extending Creativity: Large Language Models and the Practice of Poetry Translation. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 787–799, Tilburg, The Netherlands. European Association for Machine Translation.