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Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10424463" target="_blank" >RIV/00216208:11320/20:10424463 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.aclweb.org/anthology/2020.acl-srw.34/" target="_blank" >https://www.aclweb.org/anthology/2020.acl-srw.34/</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.18653/v1/2020.acl-srw.34" target="_blank" >10.18653/v1/2020.acl-srw.34</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining

  • Original language description

    Existing models of multilingual sentence embeddings require large parallel data resources which are not available for low-resource languages. We propose a novel unsupervised method to derive multilingual sentence embeddings relying only on monolingual data. We first produce a synthetic parallel corpus using unsupervised machine translation, and use it to fine-tune a pretrained cross-lingual masked language model (XLM) to derive the multilingual sentence representations. The quality of the representations is evaluated on two parallel corpus mining tasks with improvements of up to 22 F1 points over vanilla XLM. In addition, we observe that a single synthetic bilingual corpus is able to improve results for other language pairs.

  • 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/GX19-26934X" target="_blank" >GX19-26934X: Neural Representations in Multi-modal and Multi-lingual Modeling</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

    Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

  • ISBN

    978-1-952148-03-3

  • ISSN

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    255-262

  • Publisher name

    Association for Computational Linguistics

  • Place of publication

    Stroudsburg, PA, USA

  • Event location

    Online

  • Event date

    Jul 5, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article