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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • e-ISSN

  • 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