All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A8SAHFFQU" target="_blank" >RIV/00216208:11320/22:8SAHFFQU - isvavai.cz</a>

  • Result on the web

    <a href="https://aclanthology.org/2022.findings-naacl.87" target="_blank" >https://aclanthology.org/2022.findings-naacl.87</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.18653/v1/2022.findings-naacl.87" target="_blank" >10.18653/v1/2022.findings-naacl.87</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks

  • Original language description

    Code-switching dependency parsing stands as a challenging task due to both the scarcity of necessary resources and the structural difficulties embedded in code-switched languages. In this study, we introduce novel sequence labeling models to be used as auxiliary tasks for dependency parsing of code-switched text in a semi-supervised scheme. We show that using auxiliary tasks enhances the performance of an LSTM-based dependency parsing model and leads to better results compared to an XLM-R-based model with significantly less computational and time complexity. As the first study that focuses on multiple code-switching language pairs for dependency parsing, we acquire state-of-the-art scores on all of the studied languages. Our best models outperform the previous work by 7.4 LAS points on average.

  • 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

    2022

  • 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: NAACL 2022

  • ISBN

    978-1-955917-76-6

  • ISSN

  • e-ISSN

  • Number of pages

    13

  • Pages from-to

    1159-1171

  • Publisher name

    Association for Computational Linguistics

  • Place of publication

  • Event location

    Seattle, United States

  • Event date

    Jan 1, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article