Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10427102" target="_blank" >RIV/00216208:11320/19:10427102 - isvavai.cz</a>
Result on the web
<a href="https://www.aclweb.org/anthology/D19-1077" target="_blank" >https://www.aclweb.org/anthology/D19-1077</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
Original language description
Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task. This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing. We compare mBERT with the best-published methods for zero-shot cross-lingual transfer and find mBERT competitive on each task. Additionally, we investigate the most effective strategy for utilizing mBERT in this manner, determine to what extent mBERT generalizes away from language specific features, and measure factors that influence cross-lingual transfer.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
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
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů