A Balanced Data Approach for Evaluating Cross-Lingual Transfer: Mapping the Linguistic Blood Bank
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10457042" target="_blank" >RIV/00216208:11320/22:10457042 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2022.naacl-main.361.pdf" target="_blank" >https://aclanthology.org/2022.naacl-main.361.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
A Balanced Data Approach for Evaluating Cross-Lingual Transfer: Mapping the Linguistic Blood Bank
Original language description
We show that the choice of pretraining languages affects downstream cross-lingual transfer for BERT-based models. We inspect zero-shot performance in balanced data conditions to mitigate data size confounds, classifying pretraining languages that improve downstream performance as donors, and languages that are improved in zero-shot performance as recipients. We develop a method of quadratic time complexity in the number of languages to estimate these relations, instead of an exponential exhaustive computation of all possible combinations. We find that our method is effective on a diverse set of languages spanning different linguistic features and two downstream tasks. Our findings can inform developers of large-scale multilingual language models in choosing better pretraining configurations.
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
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
ISBN
978-1-955917-71-1
ISSN
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e-ISSN
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Number of pages
13
Pages from-to
4903-4915
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsburg, PA, USA
Event location
Seattle, WA, USA
Event date
Jul 10, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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