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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

978-1-956792-00-3

ISSN

—

e-ISSN

—

Počet stran výsledku

2

Strana od-do

5869-5870

Název nakladatele

International Joint Conferences on Artificial Intelligence

Místo vydání

Vienna

Místo konání akce

Vienna

Datum konání akce

23. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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