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Publication detail
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" }
Documents
_IJCAI_2022_PhD_forum__Branislav_Pecher.pdf