Super donors and super recipients: Studying cross-lingual transfer between high-resource and low-resource languages

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AGN3UV9ID" target="_blank" >RIV/00216208:11320/25:GN3UV9ID - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204885373&partnerID=40&md5=1535abf030fccace256aa1c22aee7897" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204885373&partnerID=40&md5=1535abf030fccace256aa1c22aee7897</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Super donors and super recipients: Studying cross-lingual transfer between high-resource and low-resource languages

Original language description

Despite the increasing popularity of multilingualism within the NLP community, numerous languages continue to be underrepresented due to the lack of available resources. Our work addresses this gap by introducing experiments on cross-lingual transfer between 158 high-resource (HR) and 31 low-resource (LR) languages. We mainly focus on extremely LR languages, some of which are first presented in research works. Across 158 ∗ 31 HR-LR language pairs, we investigate how continued pretraining on different HR languages affects the mT5 model's performance in representing LR languages in the LM setup. Our findings surprisingly reveal that the optimal language pairs with improved performance do not necessarily align with direct linguistic motivations, with subtoken overlap playing a more crucial role. Our investigation indicates that specific languages tend to be almost universally beneficial for pretraining (super donors), while others benefit from pretraining with almost any language (super recipients). This pattern recurs in various setups and is unrelated to the linguistic similarity of HR-LR pairs. Furthermore, we perform evaluation on two downstream tasks, part-of-speech (POS) tagging and machine translation (MT), showing how HR pretraining affects LR language performance. © 2024 Association for Computational Linguistics.

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

2024

Confidentiality

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

Article name in the collection

LoResMT - Workshop Technol. Mach. Transl. Low-Resour. Lang., Proc. Workshop

ISBN

979-889176149-0

ISSN

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e-ISSN

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Number of pages

11

Pages from-to

175-185

Publisher name

Association for Computational Linguistics (ACL)

Place of publication

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Event location

Bangkok

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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