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arXiv · 2009.07473

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
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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"\cite{DaSanMartinoSemeval20task11}. 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.

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Mayank Raj, Ajay Jaiswal, Rohit R. R, Ankita Gupta, Sudeep Kumar Sahoo, Vertika Srivastava, Yeon Hyang Kim. 2020-09-16. Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles. https://arxiv.org/abs/2009.07473

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