Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles

Mayank Raj, Ajay Jaiswal, Rohit R.R, Ankita Gupta, Sudeep Kumar Sahoo, Vertika Srivastava, Yeon Hyang Kim


Abstract
This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 ”Detection of Propaganda Techniques in News Articles”. We participated in Task ”Technique Classification” (TC) which is a multi-class classification task. To address the TC task, we used RoBERTa based transformer architecture for fine-tuning on the propaganda dataset. The predictions of RoBERTa were further fine-tuned by class-dependent-minority-class classifiers. A special classifier, which employs dynamically adapted Least Common Sub-sequence algorithm, is used to adapt to the intricacies of repetition class. Compared to the other participating systems, our submission is ranked 4th on the leaderboard.
Anthology ID:
2020.semeval-1.236
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1802–1807
Language:
URL:
https://aclanthology.org/2020.semeval-1.236
DOI:
10.18653/v1/2020.semeval-1.236
Bibkey:
Cite (ACL):
Mayank Raj, Ajay Jaiswal, Rohit R.R, Ankita Gupta, Sudeep Kumar Sahoo, Vertika Srivastava, and Yeon Hyang Kim. 2020. Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1802–1807, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles (Raj et al., SemEval 2020)
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PDF:
https://aclanthology.org/2020.semeval-1.236.pdf