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.

Large multilingual machine translation efforts are driving improved access and performance for under-resourced languages, but often fail to translate culturally specific and local concepts. Additionally, translation from practically relevant input languages may flag behind those that are comparatively over-represented in the training dataset. In this work, we release a new corpus, ZenaMT, containing 7,561 parallel Ligurian-Italian sentences, nearly a fifth of which are also translated in English. This corpus spans five domains: local and international news, Ligurian literature, Genoese Ligurian linguistics concepts, traditional card game rules, and Ligurian geographic expressions. We find that a translation model augmented with ZenaMT improves a baseline by 20%, and by over 25% (BLEU) compared to NLLB-3.3B, which is over 50 times the size. Our results demonstrate the utility of creating data sets for MT that are specifically tailored for the cultural context of Ligurian speakers. We freely release ZenaMT and expect to periodically update the corpus to improve MT performance and domain coverage.