Publication detail

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

PECHER, B. SRBA, I. BIELIKOVÁ, M.

Original Title

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

Type

conference paper

Language

English

Original 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.

Keywords

Artificial intelligence, Classification (of information), Text processing

Authors

PECHER, B.; SRBA, I.; BIELIKOVÁ, M.

Released

28. 7. 2022

Publisher

International Joint Conferences on Artificial Intelligence

Location

Vienna

ISBN

978-1-956792-00-3

Book

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

Pages from

5869

Pages to

5870

Pages count

2

URL

https://www.ijcai.org/proceedings/2022/837

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"
}