We investigate automatic attitude detection in UN Security Council speeches using adapters. Following Martin and White’s Appraisal Theory, we identify three types of evaluative language: affect (emotional responses such as hope or concern), judgement (ethical evaluations of behavior), and appreciation (valuations of objects or situations). Training only 0.95% of BERT-large’s parameters, adapters achieve F1 scores ranging from 0.76 (affect) to 0.46 (appreciation), approaching full fine-tuning performance while enabling rapid task-specific experimentation. Differences in observed evaluation metrics mirror the pattern of the human inter-annotator agreement. This correlation suggests that computational difficulty reflects genuine linguistic ambiguity. Affect benefits from conventionalized diplomatic expressions, while appreciation faces context-dependent evaluation and severe class imbalance. Analysis demonstrates that evaluative intensity varies systematically across diplomatic contexts, with implications for corpus design in specialized discourse analysis.

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.

We present Charles Translator, a machine translation system between Ukrainian and Czech, developed as part of a society-wide effort to mitigate the impact of the Russian-Ukrainian war on individuals and society. The system was developed in the spring of 2022 with the help of many language data providers in order to quickly meet the demand for such a service, which was not available at the time in the required quality. The translator was later implemented as an online web interface and as an Android app with speech input, both featuring Cyrillic-Latin script transliteration. The system translates directly, in comparison to other available systems that use English as a pivot, and thus makes advantage of the typological similarity of the two languages. It uses the block back-translation method which allows for efficient use of monolingual training data. The paper describes the development process including data collection and implementation, evaluation, mentions several use cases and outlines possibilities for further development of the system for educational purposes.

This paper presents CoDipA UNSC 1.0, a Corpus of Diplomatic Attitudes of the United Nations Security Council annotated with the attitude-part of the Appraisal theory. The speeches were manually selected according to topic-related and temporal criteria. The texts were then annotated according to the predefined annotation scenario. The distinguishing features of the diplomatic texts require a modified approach to attitude evaluation, which was implemented and presented in the current work. The corpus analysis has proven diplomatic speeches to be consistently evaluative, offered an overview of the most prominent means of expressing subjectivity in the corpus, and provided the results of the inter-annotator agreement evaluation.