Solomon at SemEval-2020 Task 11: Ensemble Architecture for Fine-Tuned Propaganda Detection in News Articles
This work addresses propaganda detection for NLP researchers, but it is incremental as it builds on existing transformer methods with minor enhancements.
The paper tackled the problem of detecting propaganda techniques in news articles by fine-tuning a RoBERTa transformer architecture and using class-dependent-minority-class classifiers, achieving a 4th-place ranking on the SemEval-2020 Task 11 leaderboard.
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.