The Impact of Language Adapters in Cross-Lingual Transfer for NLU
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A3ABFHWDN" target="_blank" >RIV/00216208:11320/25:3ABFHWDN - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188800351&partnerID=40&md5=c94df610721d9b75fee8d27b094b9e5f" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188800351&partnerID=40&md5=c94df610721d9b75fee8d27b094b9e5f</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
The Impact of Language Adapters in Cross-Lingual Transfer for NLU
Original language description
Modular deep learning has been proposed for the efficient adaption of pre-trained models to new tasks, domains and languages. In particular, combining language adapters with task adapters has shown potential where no supervised data exists for a language. In this paper, we explore the role of language adapters in zero-shot cross-lingual transfer for natural language understanding (NLU) benchmarks. We study the effect of including a target-language adapter in detailed ablation studies with two multilingual models and three multilingual datasets. Our results show that the effect of target-language adapters is highly inconsistent across tasks, languages and models. Retaining the source-language adapter instead often leads to an equivalent, and sometimes to a better, performance. Removing the language adapter after training has only a weak negative effect, indicating that the language adapters do not have a strong impact on the predictions. © 2024 Association for Computational Linguistics.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
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Continuities
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Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
MOOMIN - Workshop Modular Open Multiling. NLP, Proc. Workshop
ISBN
979-889176084-4
ISSN
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e-ISSN
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Number of pages
20
Pages from-to
24-43
Publisher name
Association for Computational Linguistics (ACL)
Place of publication
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Event location
St. Julian's
Event date
Jan 1, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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