Publication Details

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

PECHER, B.; SRBA, I.; BIELIKOVÁ, M. Transferability and Stability of Learning With Limited Labelled Data in Multilingual Text Domain. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Doctoral Consortium. Vienna: International Joint Conferences on Artificial Intelligence, 2022. p. 5869-5870. ISBN: 978-1-956792-00-3.
Czech title
Přenositelnost a stabilita učení s omezeným množstvím označených dat ve vícejazyčné textové doméně
Type
conference paper
Language
English
Authors
URL
Keywords

Artificial intelligence, Classification (of information), Text processing

Abstract

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.

Published
2022
Pages
5869–5870
Proceedings
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Doctoral Consortium
ISBN
978-1-956792-00-3
Publisher
International Joint Conferences on Artificial Intelligence
Place
Vienna
DOI
EID Scopus
BibTeX
@inproceedings{BUT180394,
  author="PECHER, B. and SRBA, I. and BIELIKOVÁ, M.",
  title="Transferability and Stability of Learning With Limited Labelled Data in Multilingual Text Domain",
  booktitle="Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Doctoral Consortium",
  year="2022",
  pages="5869--5870",
  publisher="International Joint Conferences on Artificial Intelligence",
  address="Vienna",
  doi="10.24963/ijcai.2022/837",
  isbn="978-1-956792-00-3",
  url="https://www.ijcai.org/proceedings/2022/837"
}
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