@inproceedings{bremerman-etal-2026-translating,
title = "Translating the Untranslatable: An Operationalizable Ontology for Untranslatability",
author = "Bremerman, Jacob and
Joshi, Brihi and
Arai, Hirona Jacqueline and
Ren, Xiang and
May, Jonathan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1383/",
doi = "10.18653/2026.findings-emnlp.1383",
pages = "29405--29422",
ISBN = "979-8-89176-462-0",
abstract = "Untranslatability{---}cases where meaning cannot be directly preserved across languages{---}is well-studied in linguistics but underexplored in NLP. As machine translation (MT) systems improve on standard benchmarks, their limitations increasingly concentrate in such cases, where translation cannot be reduced to one-to-one equivalence. We introduce a structured ontology of untranslatability along with a taxonomy of compensation strategies, which are specific techniques to convey meaning under these untranslatable circumstances. We operationalize this framework into a multilingual dataset of untranslatable sentences paired with strategy-based translations, enabling controlled analysis of translation behavior. Initial human preference studies suggest that translation quality depends on the strategy used, with consistent preferences for outputs that include explanatory context, known as the Annotation compensation strategy. Our framework and dataset provide a foundation for studying and modeling strategy-informed machine translation."
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<abstract>Untranslatability—cases where meaning cannot be directly preserved across languages—is well-studied in linguistics but underexplored in NLP. As machine translation (MT) systems improve on standard benchmarks, their limitations increasingly concentrate in such cases, where translation cannot be reduced to one-to-one equivalence. We introduce a structured ontology of untranslatability along with a taxonomy of compensation strategies, which are specific techniques to convey meaning under these untranslatable circumstances. We operationalize this framework into a multilingual dataset of untranslatable sentences paired with strategy-based translations, enabling controlled analysis of translation behavior. Initial human preference studies suggest that translation quality depends on the strategy used, with consistent preferences for outputs that include explanatory context, known as the Annotation compensation strategy. Our framework and dataset provide a foundation for studying and modeling strategy-informed machine translation.</abstract>
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%0 Conference Proceedings
%T Translating the Untranslatable: An Operationalizable Ontology for Untranslatability
%A Bremerman, Jacob
%A Joshi, Brihi
%A Arai, Hirona Jacqueline
%A Ren, Xiang
%A May, Jonathan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F bremerman-etal-2026-translating
%X Untranslatability—cases where meaning cannot be directly preserved across languages—is well-studied in linguistics but underexplored in NLP. As machine translation (MT) systems improve on standard benchmarks, their limitations increasingly concentrate in such cases, where translation cannot be reduced to one-to-one equivalence. We introduce a structured ontology of untranslatability along with a taxonomy of compensation strategies, which are specific techniques to convey meaning under these untranslatable circumstances. We operationalize this framework into a multilingual dataset of untranslatable sentences paired with strategy-based translations, enabling controlled analysis of translation behavior. Initial human preference studies suggest that translation quality depends on the strategy used, with consistent preferences for outputs that include explanatory context, known as the Annotation compensation strategy. Our framework and dataset provide a foundation for studying and modeling strategy-informed machine translation.
%R 10.18653/2026.findings-emnlp.1383
%U https://aclanthology.org/2026.findings-emnlp.1383/
%U https://doi.org/10.18653/2026.findings-emnlp.1383
%P 29405-29422
Markdown (Informal)
[Translating the Untranslatable: An Operationalizable Ontology for Untranslatability](https://aclanthology.org/2026.findings-emnlp.1383/) (Bremerman et al., Findings 2026)
ACL