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Script, Language, and Labels: Overcoming Three Discrepancies for Low-Resource Language Specialization

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A4TRGGLZB" target="_blank" >RIV/00216208:11320/23:4TRGGLZB - isvavai.cz</a>

  • Result on the web

    <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167971966&partnerID=40&md5=6436df972a43f6f9853e3d1b266f7090" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167971966&partnerID=40&md5=6436df972a43f6f9853e3d1b266f7090</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Script, Language, and Labels: Overcoming Three Discrepancies for Low-Resource Language Specialization

  • Original language description

    "Although multilingual pretrained models (mPLMs) enabled support of various natural language processing in diverse languages, its limited coverage of 100+ languages lets 6500+ languages remain 'unseen'. One common approach for an unseen language is specializing the model for it as target, by performing additional masked language modeling (MLM) with the target language corpus. However, we argue that, due to the discrepancy from multilingual MLM pretraining, a naïve specialization as such can be suboptimal. Specifically, we pose three discrepancies to overcome. Script and linguistic discrepancy of the target language from the related seen languages, hinder a positive transfer, for which we propose to maximize representation similarity, unlike existing approaches maximizing overlaps. In addition, label space for MLM prediction can vary across languages, for which we propose to reinitialize top layers for a more effective adaptation. Experiments over four different language families and three tasks shows that our method improves the task performance of unseen languages with statistical significance, while previous approach fails to. Copyright © 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved."

  • 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

    2023

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    "Proc. AAAI Conf. Artif. Intell., AAAI"

  • ISBN

    978-157735880-0

  • ISSN

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    13004-13013

  • Publisher name

    AAAI Press

  • Place of publication

  • Event location

    Melaka, Malaysia

  • Event date

    Jan 1, 2023

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article