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BAD-X: Bilingual Adapters Improve Zero-Shot Cross-Lingual Transfer

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AAQKV4XT9" target="_blank" >RIV/00216208:11320/22:AQKV4XT9 - isvavai.cz</a>

  • Result on the web

    <a href="https://aclanthology.org/2022.naacl-main.130" target="_blank" >https://aclanthology.org/2022.naacl-main.130</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.18653/v1/2022.naacl-main.130" target="_blank" >10.18653/v1/2022.naacl-main.130</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    BAD-X: Bilingual Adapters Improve Zero-Shot Cross-Lingual Transfer

  • Original language description

    Adapter modules enable modular and efficient zero-shot cross-lingual transfer, where current state-of-the-art adapter-based approaches learn specialized language adapters (LAs) for individual languages. In this work, we show that it is more effective to learn bilingual language pair adapters (BAs) when the goal is to optimize performance for a particular source-target transfer direction. Our novel BAD-X adapter framework trades off some modularity of dedicated LAs for improved transfer performance: we demonstrate consistent gains in three standard downstream tasks, and for the majority of evaluated low-resource languages.

  • 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 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

  • ISBN

    978-1-955917-71-1

  • ISSN

  • e-ISSN

  • Number of pages

    9

  • Pages from-to

    1791-1799

  • 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