In this work, we introduce skLEP, the first comprehensive benchmark specifically designed for evaluating Slovak natural language understanding (NLU) models. We have compiled skLEP to encompass nine diverse tasks that span token-level, sentence-pair, and document-level challenges, thereby offering a thorough assessment of model capabilities. To create this benchmark, we curated new, original datasets tailored for Slovak and meticulously translated established English NLU resources. Within this paper, we also present the first systematic and extensive evaluation of a wide array of Slovak-specific, multilingual, and English pre-trained language models using the skLEP tasks. Finally, we also release the complete benchmark data, an open-source toolkit facilitating both fine-tuning and evaluation of models, and a public leaderboard at https://github.com/slovak-nlp/sklep in the hopes of fostering reproducibility and drive future research in Slovak NLU.

Mental health concerns have garnered increasing attention, highlighting the importance of timely and accurate identification of individual stress states as a critical research domain. This study employs the multimodal StressID dataset to evaluate the contributions of three modalities—physiological signals, video, and audio—in stress recognition tasks. A set of machine learning models, including Random Forests (RF), Support Vector Machines (SVM), Multi-Layer Perceptrons (MLP), and K-Nearest Neighbors (KNN), were trained and tested with optimized parameters for each modality. In addition, the effectiveness of different multimodal fusion strategies was systematically examined. The unimodal experiments revealed that the physiological modality achieved the highest performance in the binary stress classification task (F1-score = 0.751), whereas the audio modality outperformed the others in the three-class classification task (F1-score = 0.625). In the multimodal setting, feature-level fusion yielded stable improvements in the binary classification task, while decision-level fusion achieved superior performance in the three-class classification task (F1-score = 0.65). These findings demonstrate that multimodal integration can substantially enhance the accuracy of stress recognition. Future research directions include incorporating temporal modeling and addressing data imbalance to further improve the robustness and applicability of stress recognition systems.

This work proposes an information retrieval evaluation set for the Slovak language. A set of 80 queries written in the natural language is given together with the set of relevant documents. The document set contains 3980 newspaper articles sorted into 6 categories. Each document in the result set is manually annotated for relevancy with its corresponding query. The evaluation set is mostly compatible with the Cranfield test collection using the same methodology for queries and annotation of relevancy. In addition to that it provides annotation for document title, author, publication date and category that can be used for evaluation of automatic document clustering and categorization.

The presented corpus aims to be the first attempt to create a representative sample of the contemporary Slovak language from various domains with easy searching and automated processing. This first version of the corpus contains words and automatic morphological and named entity annotations and transcriptions of abbreviations and numerals. Integral part of the proposed paper is a word boundary and sentence boundary detection algorithm that utilizes characteristic features of the language.