Navigating Cross-Lingual Natural Language Processing: Challenges, Strategies, and Applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AI89URPFH" target="_blank" >RIV/00216208:11320/25:I89URPFH - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201101707&doi=10.1007%2f978-981-97-2716-2_19&partnerID=40&md5=39ce5825df4f63ba7fdd524197d8fdaf" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201101707&doi=10.1007%2f978-981-97-2716-2_19&partnerID=40&md5=39ce5825df4f63ba7fdd524197d8fdaf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-97-2716-2_19" target="_blank" >10.1007/978-981-97-2716-2_19</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Navigating Cross-Lingual Natural Language Processing: Challenges, Strategies, and Applications

Popis výsledku v původním jazyce

Modern techniques for most Natural Language Processing (NLP) activities have attained near-human functionality. This latest advancement has benefited countless individuals and companies throughout the globe. Unfortunately, most massive tagged databases are only accessible in a couple of languages; for many languages, either few or no tags are accessible to enable automatic NLP uses. As a result, among the priorities of cross-lingual NLP study is to build computing algorithms that leverage abundant resource corpora of languages and employ them in limited-resource language uses through transportable illustration learning. The paper covers the basic difficulties and suggests multiple approaches for cross-lingual illustration learning that employ common syntax dependence to link typological variations across languages and efficiently use unmarked supplies to learn solid and generalizable depictions. The methodologies suggested in this research efficiently translate across a broad spectrum of languages and NLP uses such as dependent parsing, titled entity identification, text categorization, query replying, and others. Test outcomes reveal that enhancing mBERT with syntax increases cross-lingual transfer by 1.4 and 1.6 scores on average for every targeted language in PAWS-X and MLQR, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Název v anglickém jazyce

Navigating Cross-Lingual Natural Language Processing: Challenges, Strategies, and Applications

Popis výsledku anglicky

Modern techniques for most Natural Language Processing (NLP) activities have attained near-human functionality. This latest advancement has benefited countless individuals and companies throughout the globe. Unfortunately, most massive tagged databases are only accessible in a couple of languages; for many languages, either few or no tags are accessible to enable automatic NLP uses. As a result, among the priorities of cross-lingual NLP study is to build computing algorithms that leverage abundant resource corpora of languages and employ them in limited-resource language uses through transportable illustration learning. The paper covers the basic difficulties and suggests multiple approaches for cross-lingual illustration learning that employ common syntax dependence to link typological variations across languages and efficiently use unmarked supplies to learn solid and generalizable depictions. The methodologies suggested in this research efficiently translate across a broad spectrum of languages and NLP uses such as dependent parsing, titled entity identification, text categorization, query replying, and others. Test outcomes reveal that enhancing mBERT with syntax increases cross-lingual transfer by 1.4 and 1.6 scores on average for every targeted language in PAWS-X and MLQR, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Druh

D - Stať ve sborníku

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

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Smart Innov. Syst. Technol.

ISBN

978-981972715-5

ISSN

2190-3018

e-ISSN

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Počet stran výsledku

12

Strana od-do

203-214

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

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Místo konání akce

Noida

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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