Specializing Multilingual Language Models: An Empirical Study

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10442245" target="_blank" >RIV/00216208:11320/21:10442245 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Specializing Multilingual Language Models: An Empirical Study

Original language description

Pretrained multilingual language models have become a common tool in transferring NLP capabilities to low-resource languages, often with adaptations. In this work, we study the performance, extensibility, and interaction of two such adaptations: vocabulary augmentation and script transliteration. Our evaluations on part-of-speech tagging, universal dependency parsing, and named entity recognition in nine diverse low-resource languages uphold the viability of these approaches while raising new questions around how to optimally adapt multilingual models to low-resource settings.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

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Continuities

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Publication year

2021

Confidentiality

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

Article name in the collection

Proceedings of the 1st Workshop on Multilingual Representation Learning

ISBN

978-1-954085-96-1

ISSN

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e-ISSN

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Number of pages

11

Pages from-to

51-61

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg

Event location

Punta Cana

Event date

Nov 11, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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