AdaptOr: Objective-Centric Adaptation Framework for Language
Models

D 2022

AdaptOr: Objective-Centric Adaptation Framework for Language Models

ŠTEFÁNIK, Michal, Vít NOVOTNÝ, Nikola GROVEROVÁ a Petr SOJKA

Základní údaje

Originální název

AdaptOr: Objective-Centric Adaptation Framework for Language Models

Autoři

ŠTEFÁNIK, Michal (703 Slovensko, garant, domácí), Vít NOVOTNÝ (203 Česká republika, domácí), Nikola GROVEROVÁ (203 Česká republika) a Petr SOJKA (203 Česká republika, domácí)

Vydání

Dublin, Irsko, Proceedings of the 60th Conference of Association of Computational Linguistics, ACL 2022, od s. 261-269, 9 s. 2022

Nakladatel

Association for Computational Linguistics, ACL

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

paper with code Preprint publisher's PDF

Kód RIV

RIV/00216224:14330/22:00125674

Organizační jednotka

Fakulta informatiky

ISBN

978-1-955917-24-7

ISSN

DOI

http://dx.doi.org/10.18653/v1/2022.acl-demo.26

UT WoS

000828759800025

Klíčová slova česky

plnotextové vyhledávání; doménová adaptace

Klíčová slova anglicky

Adaptor library; domain adaptation; similarity search; vector space; embeddings

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 14. 5. 2024 09:59, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

MUNI/A/1230/2021, interní kód MU

Název: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 22 (Akronym: SKOMU)

Investor: Masarykova univerzita, Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 22

Citovat

ŠTEFÁNIK, Michal, Vít NOVOTNÝ, Nikola GROVEROVÁ a Petr SOJKA. AdaptOr: Objective-Centric Adaptation Framework for Language Models. Online. In Valerio Basile, Zornitsa Kozareva, Sanja Stajner. Proceedings of the 60th Conference of Association of Computational Linguistics, ACL 2022. Dublin, Irsko: Association for Computational Linguistics, ACL, 2022, s. 261-269. ISBN 978-1-955917-24-7. Dostupné z: https://dx.doi.org/10.18653/v1/2022.acl-demo.26.

@inproceedings{1847597,
   author = {Štefánik, Michal and Novotný, Vít and Groverová, Nikola and Sojka, Petr},
   address = {Dublin, Irsko},
   booktitle = {Proceedings of the 60th Conference of Association of Computational Linguistics, ACL 2022},
   doi = {http://dx.doi.org/10.18653/v1/2022.acl-demo.26},
   editor = {Valerio Basile, Zornitsa Kozareva, Sanja Stajner},
   keywords = {Adaptor library; domain adaptation; similarity search; vector space; embeddings},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Dublin, Irsko},
   isbn = {978-1-955917-24-7},
   pages = {261-269},
   publisher = {Association for Computational Linguistics, ACL},
   title = {AdaptOr: Objective-Centric Adaptation Framework for Language Models},
   url = {https://paperswithcode.com/paper/adapt-mathcal-o-r-objective-centric},
   year = {2022}
}

TY  - JOUR
ID  - 1847597
AU  - Štefánik, Michal - Novotný, Vít - Groverová, Nikola - Sojka, Petr
PY  - 2022
TI  - AdaptOr: Objective-Centric Adaptation Framework for Language Models
PB  - Association for Computational Linguistics, ACL
CY  - Dublin, Irsko
SN  - 9781955917247
KW  - Adaptor library
KW  - domain adaptation
KW  - similarity search
KW  - vector space
KW  - embeddings
UR  - https://paperswithcode.com/paper/adapt-mathcal-o-r-objective-centric
N2  - 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.
ER  -

ŠTEFÁNIK, Michal, Vít NOVOTNÝ, Nikola GROVEROVÁ a Petr SOJKA. AdaptOr: Objective-Centric Adaptation Framework for Language Models. Online. In Valerio Basile, Zornitsa Kozareva, Sanja Stajner. \textit{Proceedings of the 60th Conference of Association of Computational Linguistics, ACL 2022}. Dublin, Irsko: Association for Computational Linguistics, ACL, 2022, s.~261-269. ISBN~978-1-955917-24-7. Dostupné z: https://dx.doi.org/10.18653/v1/2022.acl-demo.26.

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