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Zero-shot cross-lingual dependency parsing through contextual embedding transformation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10440973" target="_blank" >RIV/00216208:11320/21:10440973 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Zero-shot cross-lingual dependency parsing through contextual embedding transformation

  • Original language description

    Linear embedding transformation has been shown to be effective for zero-shot cross-lingual transfer tasks and achieve surprisingly promising results. However, cross-lingual embedding space mapping is usually studied in static word-level embeddings, where a space transformation is derived by aligning representations of translation pairs that are referred from dictionaries. We move further from this line and investigate a contextual embedding alignment approach which is sense-level and dictionary-free. To enhance the quality of the mapping, we also provide a deep view of properties of contextual embeddings, i.e., the anisotropy problem and its solution. Experiments on zero-shot dependency parsing through the concept-shared space built by our embedding transformation substantially outperform state-of-the-art methods using multilingual embeddings.

  • 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

  • Continuities

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Adapt-NLP 2021 - 2nd Workshop on Domain Adaptation for NLP, Proceedings

  • ISBN

    978-1-954085-08-4

  • ISSN

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    204-213

  • Publisher name

    Association for Computational Linguistics

  • Place of publication

    Stroudsburg

  • Event location

    online

  • Event date

    Apr 20, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article