This paper describes linguistic and technological challenges encountered within an applied project aimed at expanding a large e-learning portal from its original Czech to three other languages: Ukrainian, English and German. Although there seems to be a general belief that machine translation is a solved task in 2026, we show that translating educational content, which in our case is highly terminological, multimodal, interactive and encoded in XML, brings along many challenges of different types, some easily solvable and some not. We also compare our results from the early phase of the project (Transformer-based machine translation) with those after the switch to the LLM-based translation methods. We show that both MT methods are prone to different types of errors, some of which are quite new (such as the undesired correction of counterfactual statements) and require new ways of handling them. The resulting four-language edition of the educational web portal will be freely available to educators, students and researchers by the end of 2026.

The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved.

This paper provides a quick overview of possible methods how to detect that reference translations were actually created by post-editing an MT system. Two methods based on automatic metrics are presented: BLEU difference between the suspected MT and some other good MT and BLEU difference using additional references. These two methods revealed a suspicion that the WMT 2020 Czech reference is based on MT. The suspicion was confirmed in a manual analysis by finding concrete proofs of the post-editing procedure in particular sentences. Finally, a typology of post-editing changes is presented where typical errors or changes made by the post-editor or errors adopted from the MT are classified.