Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F19%3AA20020EU" target="_blank" >RIV/61988987:17610/19:A20020EU - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.18653/v1/P19-1439" target="_blank" >http://dx.doi.org/10.18653/v1/P19-1439</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/P19-1439" target="_blank" >10.18653/v1/P19-1439</a>
Alternative languages
Result language
angličtina
Original language name
Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
Original language description
Natural language understanding has recently seen a surge of progress with the use of sentence encoders like ELMo (Peters et al., 2018a) and BERT (Devlin et al., 2019) which are pretrained on variants of language modeling. We conduct the first large-scale systematic study of candidate pretraining tasks, comparing 19 different tasks both as alternatives and complements to language modeling. Our primary results support the use language modeling, especially when combined with pretraining on additional labeled-data tasks. However, our results are mixed across pretraining tasks and show some concerning trends: In ELMo's pretrain-then-freeze paradigm, random baselines are worryingly strong and results vary strikingly across target tasks. In addition, fine-tuning BERT on an intermediate task often negatively impacts downstream transfer. In a more positive trend, we see modest gains from multitask training, suggesting the development of more sophisticated multitask and transfer learning techniques as an avenue for further research.
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
2019
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 57th Conference of the Association for Computational Linguistics
ISBN
978-1-950737-48-2
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
4465-4476
Publisher name
Association for Computational Linguistics 2019
Place of publication
Florence
Event location
Florence
Event date
Jul 28, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000493046106098