Improving the Performance of UDify with Linguistic Typology Knowledge
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10442296" target="_blank" >RIV/00216208:11320/21:10442296 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Improving the Performance of UDify with Linguistic Typology Knowledge
Original language description
UDify is the state-of-the-art language-agnostic dependency parser which is trained on a polyglot corpus of 75 languages. This multilingual modeling enables the model to generalize over unknown/lesser-known languages, thus leading to improved performance on low-resource languages. In this work we used linguistic typology knowledge available in URIEL database, to improve the cross-lingual transferring ability of UDify even further.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Third Workshop on Computational Typology and Multilingual NLP
ISBN
978-1-954085-34-3
ISSN
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e-ISSN
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Number of pages
23
Pages from-to
38-60
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsburg
Event location
online
Event date
Jun 10, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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