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A BERT's Eye View: Identification of Irish Multiword Expressions Using Pre-trained Language Models

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AQCXFI9DD" target="_blank" >RIV/00216208:11320/22:QCXFI9DD - isvavai.cz</a>

  • Result on the web

    <a href="https://aclanthology.org/2022.mwe-1.13" target="_blank" >https://aclanthology.org/2022.mwe-1.13</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    A BERT's Eye View: Identification of Irish Multiword Expressions Using Pre-trained Language Models

  • Original language description

    This paper reports on the investigation of using pre-trained language models for the identification of Irish verbal multiword expressions (vMWEs), comparing the results with the systems submitted for the PARSEME shared task edition 1.2. We compare the use of a monolingual BERT model for Irish (gaBERT) with multilingual BERT (mBERT), fine-tuned to perform MWE identification, presenting a series of experiments to explore the impact of hyperparameter tuning and dataset optimisation steps on these models. We compare the results of our optimised systems to those achieved by other systems submitted to the shared task, and present some best practices for minority languages addressing this task.

  • 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

    Proceedings of the 18th Workshop on Multiword Expressions (MWE 2022) n@LREC2022

  • ISBN

    979-10-95546-90-0

  • ISSN

  • e-ISSN

  • Number of pages

    11

  • Pages from-to

    89-99

  • Publisher name

    European Language Resources Association

  • Place of publication

  • Event location

    Marseille, France

  • Event date

    Jan 1, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article