  Propaganda spreads the ideology and beliefs of like-minded people,
brainwashing their audiences, and sometimes leading to violence. SemEval 2020
Task-11 aims to design automated systems for news propaganda detection. Task-11
consists of two sub-tasks, namely, Span Identification - given any news
article, the system tags those specific fragments which contain at least one
propaganda technique; and Technique Classification - correctly classify a given
propagandist statement amongst 14 propaganda techniques. For sub-task 1, we use
contextual embeddings extracted from pre-trained transformer models to
represent the text data at various granularities and propose a
multi-granularity knowledge sharing approach. For sub-task 2, we use an
ensemble of BERT and logistic regression classifiers with linguistic features.
Our results reveal that the linguistic features are the strong indicators for
covering minority classes in a highly imbalanced dataset.
