Probing Linguistic Knowledge in Italian Neural Language Models across Language Varieties
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AJMNY7VL7" target="_blank" >RIV/00216208:11320/22:JMNY7VL7 - isvavai.cz</a>
Result on the web
<a href="https://journals.openedition.org/ijcol/965" target="_blank" >https://journals.openedition.org/ijcol/965</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4000/ijcol.965" target="_blank" >10.4000/ijcol.965</a>
Alternative languages
Result language
angličtina
Original language name
Probing Linguistic Knowledge in Italian Neural Language Models across Language Varieties
Original language description
In this paper, we present an in-depth investigation of the linguistic knowledge encoded by the transformer models currently available for the Italian language. In particular, we investigate how the complexity of two different architectures of probing models affects the performance of the Transformers in encoding a wide spectrum of linguistic features. Moreover, we explore how this implicit knowledge varies according to different textual genres and language varieties.
Czech name
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Czech description
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Classification
Type
J<sub>ost</sub> - Miscellaneous article in a specialist periodical
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
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IJCoL. Italian Journal of Computational Linguistics
ISSN
2499-4553
e-ISSN
1664-1078
Volume of the periodical
8
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
25-44
UT code for WoS article
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EID of the result in the Scopus database
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