Typological Challenges for the Application of Multilingual Language Models in the Digital Humanities

Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3ANG2I82PF" target="_blank" >RIV/00216208:11320/23:NG2I82PF - isvavai.cz</a>
Výsledek na webu
<a href="https://www.taylorfrancis.com/chapters/edit/10.4324/9781003393696-13/typological-challenges-application-multilingual-language-models-digital-humanities-marcell-richard-fekete-johannes-bjerva-lisa-beinborn" target="_blank" >https://www.taylorfrancis.com/chapters/edit/10.4324/9781003393696-13/typological-challenges-application-multilingual-language-models-digital-humanities-marcell-richard-fekete-johannes-bjerva-lisa-beinborn</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4324/9781003393696-13" target="_blank" >10.4324/9781003393696-13</a>

Jazyk výsledku
angličtina
Název v původním jazyce
Typological Challenges for the Application of Multilingual Language Models in the Digital Humanities
Popis výsledku v původním jazyce
"This chapter explores the challenges faced by multilingual digital humanities (DH) researchers when using natural language processing (NLP) techniques in their work. While recent developments in neural language models have improved the usefulness of NLP tools for DH experts working with complex datasets, there is a bias towards languages in the Global North, particularly English. This bias arises from data imbalance, unequal script, and vocabulary coverage, and differences in linguistic typology, leading to poor performance for most of the world’s languages."
Název v anglickém jazyce
Typological Challenges for the Application of Multilingual Language Models in the Digital Humanities
Popis výsledku anglicky
"This chapter explores the challenges faced by multilingual digital humanities (DH) researchers when using natural language processing (NLP) techniques in their work. While recent developments in neural language models have improved the usefulness of NLP tools for DH experts working with complex datasets, there is a bias towards languages in the Global North, particularly English. This bias arises from data imbalance, unequal script, and vocabulary coverage, and differences in linguistic typology, leading to poor performance for most of the world’s languages."

Druh
C - Kapitola v odborné knize
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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