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

arXiv: https://arxiv.org/abs/2009.07473 · HTML: https://mlsanity.com/paper/2009.07473/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [RadBERT-CL: Factually-Aware Contrastive Learning For Radiology Report Classification](https://mlsanity.com/paper/2110.15426/) (2021)
- [Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances](https://mlsanity.com/paper/2212.02675/) (2022)
- [Sharif-MGTD at SemEval-2024 Task 8: A Transformer-Based Approach to Detect Machine Generated Text](https://mlsanity.com/paper/2407.11774/) (2024)
- [Hierarchical Multi-Instance Multi-Label Learning for Detecting Propaganda Techniques](https://mlsanity.com/paper/2305.19419/) (2023)

## Abstract (arXiv)

This paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 "Detection of Propaganda Techniques in News Articles"citeDaSanMartinoSemeval20task11. 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.