Named Entity Recognition for Low-Resource Languages - Profiting from Language Families

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AXLWH28K3" target="_blank" >RIV/00216208:11320/23:XLWH28K3 - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175337793&partnerID=40&md5=3aff693e3e64582a78b502f12caa38ae" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175337793&partnerID=40&md5=3aff693e3e64582a78b502f12caa38ae</a>

DOI - Digital Object Identifier

—

Result language

angličtina

Original language name

Named Entity Recognition for Low-Resource Languages - Profiting from Language Families

Original language description

"Machine learning drives forward the development in many areas of Natural Language Processing (NLP). Until now, many NLP systems and research are focusing on high-resource languages, i.e. languages for which many data resources exist. Recently, so-called low-resource languages increasingly come into focus. In this context, multi-lingual language models, which are trained on related languages to a target low-resource language, may enable NLP tasks on this low-resource language. In this work, we investigate the use of multi-lingual models for Named Entity Recognition (NER) for low-resource languages. We consider the West Slavic language family and the low-resource languages Upper Sorbian and Kashubian. Three RoBERTa models were trained from scratch, two mono-lingual models for Czech and Polish, and one bi-lingual model for Czech and Polish. These models were evaluated on the NER downstream task for Czech, Polish, Upper Sorbian, and Kashubian, and compared to existing state-of-the-art models such as RobeCzech, HerBERT, and XLM-R. The results indicate that the mono-lingual models perform better on the language they were trained on, and both the mono-lingual and language family models outperform the large multi-lingual model in downstream tasks. Overall, the study shows that low-resource West Slavic languages can benefit from closely related languages and their models. © 2023 Association for Computational Linguistics."

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

—

Publication year

2023

Confidentiality

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

Article name in the collection

"EACL - Workshop Slav. Nat. Lang. Process., Proc. SlavicNLP"

ISBN

978-195942957-9

ISSN

—

e-ISSN

—

Number of pages

10

Pages from-to

1-10

Publisher name

Association for Computational Linguistics

Place of publication

—

Event location

Melaka, Malaysia

Event date

Jan 1, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—