Context-aware Neural Machine Translation can potentially enhance automated translation quality through effective modelling of context beyond the sentence level. However, suitable corpora for contextual modelling are still scarce, presenting a significant challenge for the training and evaluation of context-aware systems. To address this challenge, we describe tando \({}^{+}\) , a document-level corpus for the under-resourced language pairs Basque-French and Basque-Spanish. We provide a detailed description of this corpus, which is to be shared with the scientific community. The corpus comprises parallel data from diverse domains (literature, subtitles, and news) and incorporates context-level information. Additionally, it provides manually crafted contrastive test sets for Basque-Spanish, designed for comprehensive assessment of gender and register contextual phenomena. Additionally, we train and evaluate sentence-level baseline models and several state-of-the-art contextual variants. Our results and analyses indicate that the corpus is well-suited to train and evaluate context-aware machine translation systems for the two selected under-resourced language pairs.
