Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles
This work addresses propaganda detection for media analysis, but it is incremental as it builds on existing BERT models with added features and data augmentation.
The paper tackled the problem of detecting propaganda techniques in news articles by developing BERT-based models with handcrafted features, achieving performance well above baselines in span identification and technique classification tasks.
This paper presents our systems for SemEval 2020 Shared Task 11: Detection of Propaganda Techniques in News Articles. We participate in both the span identification and technique classification subtasks and report on experiments using different BERT-based models along with handcrafted features. Our models perform well above the baselines for both tasks, and we contribute ablation studies and discussion of our results to dissect the effectiveness of different features and techniques with the goal of aiding future studies in propaganda detection.