UMSIForeseer at SemEval-2020 Task 11: Propaganda Detection by Fine-Tuning BERT with Resampling and Ensemble Learning

Yunzhe Jiang, Cristina Garbacea, Qiaozhu Mei


Abstract
We describe our participation at the SemEval 2020 “Detection of Propaganda Techniques in News Articles” - Techniques Classification (TC) task, designed to categorize textual fragments into one of the 14 given propaganda techniques. Our solution leverages pre-trained BERT models. We present our model implementations, evaluation results and analysis of these results. We also investigate the potential of combining language models with resampling and ensemble learning methods to deal with data imbalance and improve performance.
Anthology ID:
2020.semeval-1.242
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
COLING | SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1841–1846
Language:
URL:
https://aclanthology.org/2020.semeval-1.242
DOI:
10.18653/v1/2020.semeval-1.242
Bibkey:
Cite (ACL):
Yunzhe Jiang, Cristina Garbacea, and Qiaozhu Mei. 2020. UMSIForeseer at SemEval-2020 Task 11: Propaganda Detection by Fine-Tuning BERT with Resampling and Ensemble Learning. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1841–1846, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
UMSIForeseer at SemEval-2020 Task 11: Propaganda Detection by Fine-Tuning BERT with Resampling and Ensemble Learning (Jiang et al., SemEval 2020)
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PDF:
https://aclanthology.org/2020.semeval-1.242.pdf