Transferability and Stability of Learning With Limited Labelled Data in Multilingual Text Domain

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU146536" target="_blank" >RIV/00216305:26230/22:PU146536 - isvavai.cz</a>

Result on the web

<a href="https://www.ijcai.org/proceedings/2022/837" target="_blank" >https://www.ijcai.org/proceedings/2022/837</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24963/ijcai.2022/837" target="_blank" >10.24963/ijcai.2022/837</a>

Result language

angličtina

Original language name

Transferability and Stability of Learning With Limited Labelled Data in Multilingual Text Domain

Original language description

Using the learning with limited labelled data approaches to improve performance in multilingual domains, where small amount of labels are spread spread across languages and tasks, requires knowing the transferability of these approaches to new datasets and tasks. However, the lower data availability makes the learning with limited labelled data unstable, resulting in randomness invalidating the investigation, when it is not taken into consideration. Nevertheless, previous studies that perform benchmarking and investigation of such approaches mostly ignore the effects of randomness. In our work, we want to remedy this by investigating the stability and transferability, for effective use in the multilingual domains with specific characteristics.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Article name in the collection

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Doctoral Consortium

ISBN

978-1-956792-00-3

ISSN

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e-ISSN

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Number of pages

2

Pages from-to

5869-5870

Publisher name

International Joint Conferences on Artificial Intelligence

Place of publication

Vienna

Event location

Vienna

Event date

Jul 23, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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