On the Language Neutrality of Pre-trained Multilingual Representations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10424472" target="_blank" >RIV/00216208:11320/20:10424472 - isvavai.cz</a>
Result on the web
<a href="https://www.aclweb.org/anthology/2020.findings-emnlp.150/" target="_blank" >https://www.aclweb.org/anthology/2020.findings-emnlp.150/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2020.findings-emnlp.150" target="_blank" >10.18653/v1/2020.findings-emnlp.150</a>
Alternative languages
Result language
angličtina
Original language name
On the Language Neutrality of Pre-trained Multilingual Representations
Original language description
Multilingual contextual embeddings, such as multilingual BERT (mBERT) and XLM-RoBERTa, have proved useful for many multi-lingual tasks. Previous work probed the cross-linguality of the representations indirectly using zero-shot transfer learning on morphological and syntactic tasks. We instead focus on the language-neutrality of mBERT with respect to lexical semantics. Our results show that contextual embeddings are more language-neutral and in general more informative than aligned static word-type embeddings which are explicitly trained for language neutrality. Contextual embeddings are still by default only moderately language-neutral, however, we show two simple methods for achieving stronger language neutrality: first, by unsupervised centering of the representation for languages, and second by fitting an explicit projection on small parallel data. In addition, we show how to reach state-of-the-art accuracy on language identification and word alignment in parallel sentences.
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
<a href="/en/project/GA18-02196S" target="_blank" >GA18-02196S: Linguistic Structure Representation in Neural Networks</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Findings of the Association for Computational Linguistics: EMNLP 2020
ISBN
978-1-952148-90-3
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
1663-1674
Publisher name
Association for Computational Linguistics
Place of publication
Stroudsburg, PA, USA
Event location
Online
Event date
Nov 16, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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