Augmenting Text to Increase Translation Difficulty

William Kalikman, Simon Sukup, Michal Tešnar, Vilém Zouhar


Abstract
As state-of-the-art machine translation models saturate standard benchmarks, the field needs more challenging evaluations to distinguish between models of varying quality. We propose augmenting existing benchmarks to increase translation difficulty by combining adversarial optimization with a differentiable translation difficulty estimator. Our Adversarial Translation Optimization (ATO) uses gradients from a combined difficulty and fluency objective to iteratively replace tokens. Because each step branches over candidate substitutions at every position, optimization becomes a tree search problem, which we address with Beam Search. ATO offers a gradient-based alternative to LLM-based dataset creation without LLM prompting, expensive human curation, or task-specific model training. Our ATO-modified benchmark lowers average translation quality (xCOMET) from 0.93 to 0.82, compared to 0.88 for paraphrasing and 0.86 for a zero-shot baseline. Through human evaluation we confirm that the modified texts remain reasonably natural while being substantially harder to translate. We release two datasets of 200 English texts each, generated by our methods, as well as the code.
Anthology ID:
2026.eamt-1.21
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:
321–338
Language:
URL:
https://aclanthology.org/2026.eamt-1.21/
DOI:
Bibkey:
Cite (ACL):
William Kalikman, Simon Sukup, Michal Tešnar, and Vilém Zouhar. 2026. Augmenting Text to Increase Translation Difficulty. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 321–338, Tilburg, The Netherlands. European Association for Machine Translation.
Cite (Informal):
Augmenting Text to Increase Translation Difficulty (Kalikman et al., EAMT 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.eamt-1.21.pdf