  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.
