Can Pretrained English Language Models Benefit Non-English NLP Systems in Low-Resource Scenarios?

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AC6SW9ESX" target="_blank" >RIV/00216208:11320/25:C6SW9ESX - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153499625&doi=10.1109%2fTASLP.2023.3267618&partnerID=40&md5=791fbbb9ec53d2cbc190d4bb210838c1" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153499625&doi=10.1109%2fTASLP.2023.3267618&partnerID=40&md5=791fbbb9ec53d2cbc190d4bb210838c1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TASLP.2023.3267618" target="_blank" >10.1109/TASLP.2023.3267618</a>

Result language

angličtina

Original language name

Can Pretrained English Language Models Benefit Non-English NLP Systems in Low-Resource Scenarios?

Original language description

Pretrained language models have achieved great success in a wide range of natural language processing (NLP) problems, because they learn language representations from large-scale text corpora and can adapt to downstream tasks by finetuning them on annotated task data. However, such success relies on both large-scale text and annotated data, so the lack of training data is a major practical problem for many languages, especially low-resource languages. In this paper, we explore whether a pretrained English language model can benefit non-English NLP systems in low-resource scenarios, i.e., with limited text corpora or annotated data. To achieve this, we first propose cross-lingual knowledge transfer methods and then validate our methods in low-resource scenarios. Specifically, our cross-lingual knowledge transfer methods are applied in the training stages of language model pretraining or downstream finetuning. At the two stages, the methods are designed for the transfer of upstream general knowledge or downstream task-specific knowledge, respectively. In the experiments, we perform pretraining and finetuning with limited non-English data to simulate the low-resource scenarios. We evaluate our methods on ten downstream tasks over a wide range of languages, and present systematic comparisons among various knowledge transfer methods. Experimental results show that our methods successfully leverage a pretrained English language model to improve task performance in other languages. Besides, we demonstrate the multilinguality of the English language model in various application scenarios. Our findings imply the possibility to improve low-resource-language NLP systems with large-scale English language models. © 2023 IEEE.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

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

2024

Confidentiality

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

Name of the periodical

IEEE/ACM Transactions on Audio Speech and Language Processing

ISSN

2329-9290

e-ISSN

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Volume of the periodical

32

Issue of the periodical within the volume

2024

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

1061-1074

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85153499625