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AdaptOr: Objective-Centric Adaptation Framework for Language Models

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00125674" target="_blank" >RIV/00216224:14330/22:00125674 - isvavai.cz</a>

  • Result on the web

    <a href="https://aclanthology.org/2022.acl-demo.26/" target="_blank" >https://aclanthology.org/2022.acl-demo.26/</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.18653/v1/2022.acl-demo.26" target="_blank" >10.18653/v1/2022.acl-demo.26</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    AdaptOr: Objective-Centric Adaptation Framework for Language Models

  • Original language description

    Progress in natural language processing research is catalyzed by the possibilities given by the widespread software frameworks. This paper introduces the Adaptor library that transposes the traditional model-centric approach composed of pre-training + fine-tuning steps to the objective-centric approach, composing the training process by applications of selected objectives. We survey research directions that can benefit from enhanced objective-centric experimentation in multitask training, custom objectives development, dynamic training curricula, or domain adaptation. Adaptor aims to ease the reproducibility of these research directions in practice. Finally, we demonstrate the practical applicability of Adaptor in selected unsupervised domain adaptation scenarios.

  • 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

    S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

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 60th Conference of Association of Computational Linguistics, ACL 2022

  • ISBN

    9781955917247

  • ISSN

    0736-587X

  • e-ISSN

  • Number of pages

    9

  • Pages from-to

    261-269

  • Publisher name

    Association for Computational Linguistics, ACL

  • Place of publication

    Dublin, Irsko

  • Event location

    Dublin, Irsko

  • Event date

    Jan 1, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article