# aschern at SemEval-2020 Task 11: It Takes Three to Tango: RoBERTa, CRF, and Transfer Learning

arXiv: https://arxiv.org/abs/2008.02837 · HTML: https://mlsanity.com/paper/2008.02837/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation](https://mlsanity.com/paper/2203.05386/) (2022)
- [Multitask Instruction-based Prompting for Fallacy Recognition](https://mlsanity.com/paper/2301.09992/) (2023)
- [A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Models](https://mlsanity.com/paper/2410.21360/) (2024)
- [WVOQ at SemEval-2021 Task 6: BART for Span Detection and Classification](https://mlsanity.com/paper/2107.05467/) (2021)
- [NAP at SemEval-2023 Task 3: Is Less Really More? (Back-)Translation as Data Augmentation Strategies for Detecting Persuasion Techniques](https://mlsanity.com/paper/2304.14179/) (2023)
- [Hierarchical Multi-Instance Multi-Label Learning for Detecting Propaganda Techniques](https://mlsanity.com/paper/2305.19419/) (2023)
- [Reasoning for Propaganda Detection via Label-Guided CoT Optimisation](https://mlsanity.com/paper/2610.17271/) (2026)

## Abstract (arXiv)

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.