How does the task complexity of masked pretraining objectives affect downstream performance?
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AUHFEIXJP" target="_blank" >RIV/00216208:11320/23:UHFEIXJP - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175439688&partnerID=40&md5=0eb598059f3ef78ab89fa48116651a54" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175439688&partnerID=40&md5=0eb598059f3ef78ab89fa48116651a54</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
How does the task complexity of masked pretraining objectives affect downstream performance?
Original language description
"Masked language modeling (MLM) is a widely used self-supervised pretraining objective, where a model needs to predict an original token that is replaced with a mask given contexts. Although simpler and computationally efficient pretraining objectives, e.g., predicting the first character of a masked token, have recently shown comparable results to MLM, no objectives with a masking scheme actually outperform it in downstream tasks. Motivated by the assumption that their lack of complexity plays a vital role in the degradation, we validate whether more complex masked objectives can achieve better results and investigate how much complexity they should have to perform comparably to MLM. Our results using GLUE, SQuAD, and Universal Dependencies benchmarks demonstrate that more complicated objectives tend to show better downstream results with at least half of the MLM complexity needed to perform comparably to MLM. Finally, we discuss how we should pretrain a model using a masked objective from the task complexity perspective. © 2023 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
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
"Proc. Annu. Meet. Assoc. Comput Linguist."
ISBN
978-195942962-3
ISSN
0736-587X
e-ISSN
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Number of pages
11
Pages from-to
10527-10537
Publisher name
Association for Computational Linguistics (ACL)
Place of publication
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Event location
Dubrovnik
Event date
Jan 1, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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