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    <abstract>We describe Grammatist, our submission to the unconstrained track of the WMT 2026 General Machine Translation Task, developed in direct collaboration with community members of two regional languages of Italy, Ladin and Ligurian. Ladin and Ligurian represent challenging scenarios for machine translation (MT), as they have limited resources for conventional data-driven approaches. However, this scarcity of large-scale parallel data does not reflect a lack of linguistic documentation: both languages are extensively documented in dictionaries and grammar books, which contain valuable knowledge but are not readily exploitable by conventional MT approaches. Grammatist addresses this gap by making this information available to a large language model (LLM) at inference time. The framework is both agentic and model-agnostic: it dynamically consults dictionaries and grammar books during translation to retrieve the information most relevant to the input. Any LLM can serve as the underlying base model, enabling Grammatist to benefit from advances in increasingly capable models. For the WMT 2026 task, we apply Grammatist to structured document-level translation from English into Ligurian and Ladin.</abstract>
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%0 Conference Proceedings
%T Grammatist at WMT 2026 General MT Task: An Agentic LLM Framework for Machine Translation with Explicit Linguistic Knowledge
%A Frontull, Samuel
%A Maillard, Jean
%A Haberland, Christopher
%A Videsott, Ruth
%A Lusito, Stefano
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F frontull-etal-2026-grammatist
%X We describe Grammatist, our submission to the unconstrained track of the WMT 2026 General Machine Translation Task, developed in direct collaboration with community members of two regional languages of Italy, Ladin and Ligurian. Ladin and Ligurian represent challenging scenarios for machine translation (MT), as they have limited resources for conventional data-driven approaches. However, this scarcity of large-scale parallel data does not reflect a lack of linguistic documentation: both languages are extensively documented in dictionaries and grammar books, which contain valuable knowledge but are not readily exploitable by conventional MT approaches. Grammatist addresses this gap by making this information available to a large language model (LLM) at inference time. The framework is both agentic and model-agnostic: it dynamically consults dictionaries and grammar books during translation to retrieve the information most relevant to the input. Any LLM can serve as the underlying base model, enabling Grammatist to benefit from advances in increasingly capable models. For the WMT 2026 task, we apply Grammatist to structured document-level translation from English into Ligurian and Ladin.
%U https://aclanthology.org/2026.wmt-main.65/
%P 1248-1257

Markdown (Informal)

[Grammatist at WMT 2026 General MT Task: An Agentic LLM Framework for Machine Translation with Explicit Linguistic Knowledge](https://aclanthology.org/2026.wmt-main.65/) (Frontull et al., WMT 2026)

• Grammatist at WMT 2026 General MT Task: An Agentic LLM Framework for Machine Translation with Explicit Linguistic Knowledge (Frontull et al., WMT 2026)

ACL

• Samuel Frontull, Jean Maillard, Christopher Haberland, Ruth Videsott, and Stefano Lusito. 2026. Grammatist at WMT 2026 General MT Task: An Agentic LLM Framework for Machine Translation with Explicit Linguistic Knowledge. In Proceedings of the Eleventh Conference on Machine Translation, pages 1248–1257, Budapest, Hungary. Association for Computational Linguistics.