What does Chinese BERT learn about syntactic knowledge?

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

What does Chinese BERT learn about syntactic knowledge?

Popis výsledku v původním jazyce

"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."

Název v anglickém jazyce

What does Chinese BERT learn about syntactic knowledge?

Popis výsledku anglicky

"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."

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

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Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

"PeerJ Computer Science"

ISSN

2376-5992

e-ISSN

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Svazek periodika

9

Číslo periodika v rámci svazku

2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

22

Strana od-do

1-22

Kód UT WoS článku

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EID výsledku v databázi Scopus

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