ARC-NLP at CASE 2022 Task 1: Ensemble Learning for Multilingual Protest Event Detection

Umitcan Sahin, Oguzhan Ozcelik, Izzet Emre Kucukkaya, Cagri Toraman


Abstract
Automated socio-political protest event detection is a challenging task when multiple languages are considered. In CASE 2022 Task 1, we propose ensemble learning methods for multilingual protest event detection in four subtasks with different granularity levels from document-level to entity-level. We develop an ensemble of fine-tuned Transformer-based language models, along with a post-processing step to regularize the predictions of our ensembles. Our approach places the first place in 6 out of 16 leaderboards organized in seven languages including English, Mandarin, and Turkish.
Anthology ID:
2022.case-1.25
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)
Venue:
CASE
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
175–183
Language:
URL:
https://aclanthology.org/2022.case-1.25
DOI:
Bibkey:
Cite (ACL):
Umitcan Sahin, Oguzhan Ozcelik, Izzet Emre Kucukkaya, and Cagri Toraman. 2022. ARC-NLP at CASE 2022 Task 1: Ensemble Learning for Multilingual Protest Event Detection. In Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE), pages 175–183, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
ARC-NLP at CASE 2022 Task 1: Ensemble Learning for Multilingual Protest Event Detection (Sahin et al., CASE 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.case-1.25.pdf