CEIA-NLP at CASE 2022 Task 1: Protest News Detection for Portuguese

Diogo Fernandes, Adalberto Junior, Gabriel Marques, Anderson Soares, Arlindo Galvao Filho


Abstract
This paper summarizes our work on the document classification subtask of Multilingual protest news detection of the CASE @ ACL-IJCNLP 2022 workshok. In this context, we investigate the performance of monolingual and multilingual transformer-based models in low data resources, taking Portuguese as an example and evaluating language models on document classification. Our approach became the winning solution in Portuguese document classification achieving 0.8007 F1 Score on Test set. The experimental results demonstrate that multilingual models achieve best results in scenarios with few dataset samples of specific language, because we can train models using datasets from other languages of the same task and domain.
Anthology ID:
2022.case-1.26
Volume:
Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Editors:
Ali Hürriyetoğlu, Hristo Tanev, Vanni Zavarella, Erdem Yörük
Venue:
CASE
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
184–188
Language:
URL:
https://aclanthology.org/2022.case-1.26
DOI:
10.18653/v1/2022.case-1.26
Bibkey:
Cite (ACL):
Diogo Fernandes, Adalberto Junior, Gabriel Marques, Anderson Soares, and Arlindo Galvao Filho. 2022. CEIA-NLP at CASE 2022 Task 1: Protest News Detection for Portuguese. In Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), pages 184–188, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
CEIA-NLP at CASE 2022 Task 1: Protest News Detection for Portuguese (Fernandes et al., CASE 2022)
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