@inproceedings{zheng-etal-2026-model-editing,
title = "Model Editing for Targeted Translation Error Repair",
author = "Zheng, Danna and
Liu, Yang and
Liu, Zhu and
Bhat, Vimal and
Aggarwal, Manoj and
Medioni, Gerard and
Sadoughi, Najmeh",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1786/",
pages = "40684--40717",
ISBN = "979-8-89176-459-0",
abstract = "Machine translation systems still make systematic errors on idioms and other expressions that cannot be translated literally. Model editing offers a possible way to fix such recurring errors without retraining the whole model, but it has mostly been studied for monolingual factual updates rather than translation. We introduce \texttt{MEMT-bench}, the first benchmark for evaluating model editing in machine translation across multiple languages, models, and editing settings. The benchmark is built from idiomatic translation errors and tests whether an edit can correct a targeted mistranslation while preserving unrelated translation behavior. Experiments with causal tracing and representative editing methods reveal both the promise and difficulty of translation editing: edits can repair targeted errors, but maintaining cross-lingual locality and robustness under repeated edits remains challenging. Code and data are publicly available at [GitHub](https://github.com/dannalily/MEMT-Model-Editing-for-Targeted-Translation-Error-Repair/) and [Hugging Face](https://huggingface.co/datasets/Dannalily/MEMT-bench), respectively."
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<abstract>Machine translation systems still make systematic errors on idioms and other expressions that cannot be translated literally. Model editing offers a possible way to fix such recurring errors without retraining the whole model, but it has mostly been studied for monolingual factual updates rather than translation. We introduce MEMT-bench, the first benchmark for evaluating model editing in machine translation across multiple languages, models, and editing settings. The benchmark is built from idiomatic translation errors and tests whether an edit can correct a targeted mistranslation while preserving unrelated translation behavior. Experiments with causal tracing and representative editing methods reveal both the promise and difficulty of translation editing: edits can repair targeted errors, but maintaining cross-lingual locality and robustness under repeated edits remains challenging. Code and data are publicly available at [GitHub](https://github.com/dannalily/MEMT-Model-Editing-for-Targeted-Translation-Error-Repair/) and [Hugging Face](https://huggingface.co/datasets/Dannalily/MEMT-bench), respectively.</abstract>
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%0 Conference Proceedings
%T Model Editing for Targeted Translation Error Repair
%A Zheng, Danna
%A Liu, Yang
%A Liu, Zhu
%A Bhat, Vimal
%A Aggarwal, Manoj
%A Medioni, Gerard
%A Sadoughi, Najmeh
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zheng-etal-2026-model-editing
%X Machine translation systems still make systematic errors on idioms and other expressions that cannot be translated literally. Model editing offers a possible way to fix such recurring errors without retraining the whole model, but it has mostly been studied for monolingual factual updates rather than translation. We introduce MEMT-bench, the first benchmark for evaluating model editing in machine translation across multiple languages, models, and editing settings. The benchmark is built from idiomatic translation errors and tests whether an edit can correct a targeted mistranslation while preserving unrelated translation behavior. Experiments with causal tracing and representative editing methods reveal both the promise and difficulty of translation editing: edits can repair targeted errors, but maintaining cross-lingual locality and robustness under repeated edits remains challenging. Code and data are publicly available at [GitHub](https://github.com/dannalily/MEMT-Model-Editing-for-Targeted-Translation-Error-Repair/) and [Hugging Face](https://huggingface.co/datasets/Dannalily/MEMT-bench), respectively.
%U https://aclanthology.org/2026.emnlp-main.1786/
%P 40684-40717
Markdown (Informal)
[Model Editing for Targeted Translation Error Repair](https://aclanthology.org/2026.emnlp-main.1786/) (Zheng et al., EMNLP 2026)
ACL
- Danna Zheng, Yang Liu, Zhu Liu, Vimal Bhat, Manoj Aggarwal, Gerard Medioni, and Najmeh Sadoughi. 2026. Model Editing for Targeted Translation Error Repair. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 40684–40717, Budapest, Hungary. Association for Computational Linguistics.