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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

  • Czech description

Classification

  • Type

    J<sub>ost</sub> - Miscellaneous article in a specialist periodical

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

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

  • EID of the result in the Scopus database