  In this paper we describe our submission for the task of Propaganda Span
Identification in news articles. We introduce a BERT-BiLSTM based span-level
propaganda classification model that identifies which token spans within the
sentence are indicative of propaganda. The "multi-granular" model incorporates
linguistic knowledge at various levels of text granularity, including word,
sentence and document level syntactic, semantic and pragmatic affect features,
which significantly improve model performance, compared to its
language-agnostic variant. To facilitate better representation learning, we
also collect a corpus of 10k news articles, and use it for fine-tuning the
model. The final model is a majority-voting ensemble which learns different
propaganda class boundaries by leveraging different subsets of incorporated
knowledge and attains $4^{th}$ position on the test leaderboard. Our final
model and code is released at https://github.com/sopu/PropagandaSemEval2020.
