aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning
This work addresses propaganda detection in news for NLP researchers, but it is incremental as it builds on existing methods like RoBERTa and CRF with minor enhancements.
The paper tackled the problem of detecting propaganda techniques in news articles by developing ensemble models with RoBERTa, CRF layers, and transfer learning, achieving an F1 score of 0.491 for span identification (3rd out of 36 teams) and 0.62 for technique classification (2nd out of 31 teams).
We describe our system for SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. We developed ensemble models using RoBERTa-based neural architectures, additional CRF layers, transfer learning between the two subtasks, and advanced post-processing to handle the multi-label nature of the task, the consistency between nested spans, repetitions, and labels from similar spans in training. We achieved sizable improvements over baseline fine-tuned RoBERTa models, and the official evaluation ranked our system 3rd (almost tied with the 2nd) out of 36 teams on the span identification subtask with an F1 score of 0.491, and 2nd (almost tied with the 1st) out of 31 teams on the technique classification subtask with an F1 score of 0.62.