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Revisiting Tri-training of Dependency Parsers

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10442278" target="_blank" >RIV/00216208:11320/21:10442278 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Revisiting Tri-training of Dependency Parsers

  • Original language description

    We compare two orthogonal semi-supervised learning techniques, namely tri-training and pretrained word embeddings, in the task of dependency parsing. We explore language-specific FastText and ELMo embeddings and multilingual BERT embeddings. We focus on a low resource scenario as semi-supervised learning can be expected to have the most impact here. Based on treebank size and available ELMo models, we select Hungarian, Uyghur (a zero-shot language for mBERT) and Vietnamese. Furthermore, we include English in a simulated low-resource setting. We find that pretrained word embeddings make more effective use of unlabelled data than tri-training but that the two approaches can be successfully combined.

  • 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

    Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

  • ISBN

    978-1-955917-09-4

  • ISSN

  • e-ISSN

  • Number of pages

    17

  • Pages from-to

    9457-9473

  • Publisher name

    Association for Computational Linguistics

  • Place of publication

    Stroudsburg

  • Event location

    Punta Cana

  • Event date

    Nov 7, 2021

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article