Term injection in machine translation is undergoing a paradigm shift in the era of large language models (LLMs). Although recent shared-task results suggest near-saturation for sentence-level term injection from pre-defined glossaries, it remains unclear whether this also holds in more challenging settings. We address this question with a custom test set for legal translation from Italian into South Tyrolean German, a non-dominant and under-resourced language variety. We cover three terminology challenges: simple term injection, localisation of abbreviated forms, and homonym disambiguation. We focus on Reasoning Models (RMs) leveraging Test-Time Scaling, comparing them with different architectures and contributing a human analysis of reasoning traces. We find that reasoning offers little benefit for simple term insertion, but yields clear gains for semantically complex cases such as homonym disambiguation. However, human evaluation of reasoning traces shows that these gains do not necessarily reflect robust and factually grounded translation-specific reasoning. We further show that without external terminological resources, even state-of-the-art RMs struggle to retrieve correct terminology for a non-dominant variety, while NMT small models remain competitive when trained on in-domain bilingual corpora. Based on these findings, we propose data collection strategies for inducing translation-specific reasoning, frameworks for adapting to and evaluating terminology across many language varieties, and terminology challenges beyond simple term injection.

This paper illustrates the process of training and evaluating NMT systems for a language pair that includes a low-resource language variety.A parallel corpus of legal texts for Italian and South Tyrolean German has been compiled, with South Tyrolean German being the low-resourced language variety. As the size of the compiled corpus is insufficient for the training, we have combined the corpus with several parallel corpora using data weighting at sentence level. We then performed an evaluation of each combination and of two popular commercial systems.

The paper reports on the creation, annotation and curation of the MT@BZ corpus, a bilingual (Italian–South Tyrolean German) corpus of machine-translated legal texts from the officially multilingual Province of Bolzano, Italy. It is the first human error-annotated corpus (using an adapted SCATE taxonomy) of machine-translated legal texts in this language combination that includes a lesser-used standard variety. The data of the project will be made available on GitHub and another repository. The output of the customized engine achieved notably better BLEU, TER and chrF2 scores than the baseline. Over 50% of the segments needed no human revision due to customization. The most frequent error categories were mistranslations and bilingual (legal) terminology errors. Our contribution brings fine-grained insights to Machine translation evaluation research, as it concerns a less common language combination, a lesser-used language variety and a societally relevant specialized domain. Such results are necessary to implement and inform the use of MT in institutional contexts of smaller language communities.

Underlying all specialized terminology is a concept system, which is particularly important in the legal domain. However, this concept system is not explicitly available in terminology management systems. We present a tool that analyzes the relations in a terminological database and presents an interactive visualization of those relations. This tool helps terminologists manage their information, including quality assurance and analysis of the information, as well as aiding in didactic presentations of terminology work.

This paper presents the particular use of "Jibiki" (Papillon's web server development platform) for the LexALP1 project. LexALP's goal is to harmonise the terminology on spatial planning and sustainable development used within the Alpine Convention2, so that the member states are able to cooperate and communicate efficiently in the four official languages (French, German, Italian and Slovene). To this purpose, LexALP uses the Jibiki platform to build a term bank for the contrastive analysis of the specialised terminology used in six different national legal systems and four different languages. In this paper we present how a generic platform like Jibiki can cope with a new kind of dictionary.