20.500.12556/DiRROS-22874 Mono- and cross-lingual evaluation of representation language models on less-resourced languages The current dominance of large language models in natural language processing is based on their contextual awareness. For text classification, text representation models, such as ELMo, BERT, and BERT derivatives, are typically fine-tuned for a specific problem. Most existing work focuses on English; in contrast, we present a large-scale multilingual empirical comparison of several monolingual and multilingual ELMo and BERT models using 14 classification tasks in nine languages. The results show, that the choice of best model largely depends on the task and language used, especially in a cross-lingual setting. In monolingual settings, monolingual BERT models tend to perform the best among BERT models. Among ELMo models, the ones trained on large corpora dominate. Cross-lingual knowledge transfer is feasible on most tasks already in a zero-shot setting without losing much performance. monolingual models multilingual models ELMo BERT corpus cross-lingual datasets korpusi večjetični veliki modeli true false true Elsevier Nizozemska Angleški jezik Slovenski jezik © 2025 The Authors. Neznano 2025-07-07 13:51:30 2025-07-07 13:51:30 2025-10-24 03:52:24 0000-00-00 00:00:00 2026 0 0 Nasl. z nasl. zaslona; Opis vira z dne 7. 7. 2025; 1-29 str. Vol. 95, [article no.] 101852 Jan. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-09-03 2025-06-03 2025-06-27 004.8 1095-8363 10.1016/j.csl.2025.101852 241622275 https://www.sciencedirect.com/science/article/pii/S0885230825000774?via%3Dihub 1 b97715db-5b28-11f0-8080-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=33292 RAZ_Ulcar_Matej_2026.pdf RAZ_Ulcar_Matej_2026.pdf 1 B4BE1F405393A7D919CA1A369BFD46BC 7e584d7e1695fc8064709e1a2cbbf2beb35776d80326a70a088b29f75bec4869 2732259e-5b2a-11f0-8080-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=33293 Institut Jožef Stefan 0 0 0