# ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether Self-Training Helps Them

arXiv: https://arxiv.org/abs/2005.07934 · HTML: https://mlsanity.com/paper/2005.07934/

## Key facts

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

## Built on by (most cited first, 16 of 16)

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- [SheffieldVeraAI at SemEval-2023 Task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification](https://mlsanity.com/paper/2303.09421/) (2023)
- [Are Large Language Models Good at Detecting Propaganda?](https://mlsanity.com/paper/2505.13706/) (2025)
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- [GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity](https://mlsanity.com/paper/2205.15812/) (2022)
- [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)
- [When Agents Persuade: Rhetoric Generation and Mitigation in LLMs](https://mlsanity.com/paper/2603.04636/) (2026)
- [IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection](https://mlsanity.com/paper/2104.01566/) (2021)
- [Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language](https://mlsanity.com/paper/2204.10519/) (2022)
- [Hierarchical Multi-Instance Multi-Label Learning for Detecting Propaganda Techniques](https://mlsanity.com/paper/2305.19419/) (2023)
- [DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software Ecosystem](https://mlsanity.com/paper/2402.16159/) (2024)

## Abstract (arXiv)

This paper presents the winning system for the propaganda Technique Classification (TC) task and the second-placed system for the propaganda Span Identification (SI) task. The purpose of TC task was to identify an applied propaganda technique given propaganda text fragment. The goal of SI task was to find specific text fragments which contain at least one propaganda technique. Both of the developed solutions used semi-supervised learning technique of self-training. Interestingly, although CRF is barely used with transformer-based language models, the SI task was approached with RoBERTa-CRF architecture. An ensemble of RoBERTa-based models was proposed for the TC task, with one of them making use of Span CLS layers we introduce in the present paper. In addition to describing the submitted systems, an impact of architectural decisions and training schemes is investigated along with remarks regarding training models of the same or better quality with lower computational budget. Finally, the results of error analysis are presented.