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

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

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

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Návaznosti

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Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

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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Počet stran výsledku

11

Strana od-do

175-185

Název nakladatele

Association for Computational Linguistics (ACL)

Místo vydání

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Místo konání akce

Bangkok

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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