From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning

Alexandre Alcoforado, Thomas Palmeira Ferraz, Lucas Hideki Okamura, Israel Campos Fama, Arnold Moya Lavado, Bárbara Dias Bueno, Bruno Veloso, Anna Helena Reali Costa


Anthology ID:
2024.propor-1.50
Volume:
Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 1
Month:
March
Year:
2024
Address:
Santiago de Compostela, Galicia/Spain
Editors:
Pablo Gamallo, Daniela Claro, António Teixeira, Livy Real, Marcos Garcia, Hugo Gonçalo Oliveira, Raquel Amaro
Venue:
PROPOR
SIG:
Publisher:
Association for Computational Lingustics
Note:
Pages:
492–502
Language:
URL:
https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.propor-1.50/
DOI:
Bibkey:
Cite (ACL):
Alexandre Alcoforado, Thomas Palmeira Ferraz, Lucas Hideki Okamura, Israel Campos Fama, Arnold Moya Lavado, Bárbara Dias Bueno, Bruno Veloso, and Anna Helena Reali Costa. 2024. From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning. In Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 1, pages 492–502, Santiago de Compostela, Galicia/Spain. Association for Computational Lingustics.
Cite (Informal):
From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning (Alcoforado et al., PROPOR 2024)
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PDF:
https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.propor-1.50.pdf