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What does Chinese BERT learn about syntactic knowledge?

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A35ITC5RD" target="_blank" >RIV/00216208:11320/23:35ITC5RD - isvavai.cz</a>

  • Result on the web

    <a href="https://peerj.com/articles/cs-1478.pdf" target="_blank" >https://peerj.com/articles/cs-1478.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.7717/peerj-cs.1478" target="_blank" >10.7717/peerj-cs.1478</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    What does Chinese BERT learn about syntactic knowledge?

  • Original language description

    "Pre-trained language models such as Bidirectional Encoder Representations fromnTransformers (BERT) have been applied to a wide range of natural language processingn(NLP) tasks and obtained significantly positive results. A growing body of research hasninvestigated the reason why BERT is so efficient and what language knowledge BERTnis able to learn. However, most of these works focused almost exclusively on English.nFew studies have explored the language information, particularly syntactic information,nthat BERT has learned in Chinese, which is written as sequences of characters. In thisnstudy, we adopted some probing methods for identifying syntactic knowledge stored innthe attention heads and hidden states of Chinese BERT."

  • 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

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    "PeerJ Computer Science"

  • ISSN

    2376-5992

  • e-ISSN

  • Volume of the periodical

    9

  • Issue of the periodical within the volume

    2023

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    22

  • Pages from-to

    1-22

  • UT code for WoS article

  • EID of the result in the Scopus database