Mono- and cross-lingual evaluation of representation language models on less-resourced languages
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AH8HCMKZH" target="_blank" >RIV/00216208:11320/26:H8HCMKZH - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.csl.2025.101852" target="_blank" >http://dx.doi.org/10.1016/j.csl.2025.101852</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.csl.2025.101852" target="_blank" >10.1016/j.csl.2025.101852</a>
Alternative languages
Result language
angličtina
Original language name
Mono- and cross-lingual evaluation of representation language models on less-resourced languages
Original language description
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. © 2025 The Authors
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2026
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computer Speech and Language
ISSN
0885-2308
e-ISSN
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Volume of the periodical
95
Issue of the periodical within the volume
2026
Country of publishing house
US - UNITED STATES
Number of pages
25
Pages from-to
101852
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105009701388