Lifting the Curse of Multilinguality by Pre-training Modular Transformers

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AE4LEH4U7" target="_blank" >RIV/00216208:11320/23:E4LEH4U7 - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2022.naacl-main.255" target="_blank" >https://aclanthology.org/2022.naacl-main.255</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/2022.naacl-main.255" target="_blank" >10.18653/v1/2022.naacl-main.255</a>

Result language

angličtina

Original language name

Lifting the Curse of Multilinguality by Pre-training Modular Transformers

Original language description

"Multilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We address this issue by introducing language-specific modules, which allows us to grow the total capacity of the model, while keeping the total number of trainable parameters per language constant. In contrast with prior work that learns language-specific components post-hoc, we pre-train the modules of our Cross-lingual Modular (X-Mod) models from the start. Our experiments on natural language inference, named entity recognition and question answering show that our approach not only mitigates the negative interference between languages, but also enables positive transfer, resulting in improved monolingual and cross-lingual performance. Furthermore, our approach enables adding languages post-hoc with no measurable drop in performance, no longer limiting the model usage to the set of pre-trained languages."

Czech name

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Czech description

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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)

Project

—

Continuities

—

Publication year

2023

Confidentiality

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

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

17

Pages from-to

3479-3495

Publisher name

arXiv

Place of publication

Seattle, USA

Event location

Seattle, USA

Event date

Jan 1, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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