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.

Digital tools serving language revitalization tend to fall into two categories: 1) linguist-oriented documentation tools that prioritize annotation, morphological analysis, and archival preservation, and 2) community-facing applications that emphasize accessibility and language learning. Few systems integrate the former with the latter, and practical barriers — including the cost of computational expertise, single-user workflows, and limited data governance — further constrain their utility. These disconnects incur additional development and communication costs for revitalization teams consisting of linguists and community members. We introduce “langlit”, a collaborative web-based platform that attempts to tailor documentation workflows for the language revitalization context within a single system. The platform integrates a finite-state morphological analyzer with a three-tier human-in-the-loop annotation workflow, searchable corpus interfaces with multiple query modalities, interactive word construction guided by the morphological grammar, corpus-linked hypothesis tracking with provenance, and a grammar-derived editable dictionary. All components share a single underlying FST grammar, and the system supports configurable access controls, collaborative editing, and optional LLM integration with transparent data handling. Designed for redeployment across languages through a modular architecture, “langlit” is published as an open-source repository on GitHub. We situate our system within the existing landscape of revitalization tools through a comparative analysis and discuss how integrated, community-informed design can better serve the specific goals of language revitalization.

We introduce a FLORES+ dataset as an evaluation benchmark for modern Wu Chinese machine translation models and showcase its compatibility with existing Wu data. Wu Chinese is mutually unintelligible with other Sinitic languages such as Mandarin and Yue (Cantonese), but uses a set of Hanzi (Chinese characters) that profoundly overlaps with others. The population of Wu speakers is the second largest among languages in China, but the language has been suffering from significant drop in usage especially among the younger generations. We identify Wu Chinese as a textually low-resource language and address challenges for its machine translation models. Our contributions include: (1) an open-source, manually translated dataset, (2) full documentations on the process of dataset creation and validation experiments, (3) preliminary tools for Wu Chinese normalization and segmentation, and (4) benefits and limitations of our dataset, as well as implications to other low-resource languages.