  This paper describes our system (MIC-CIS) details and results of
participation in the fine-grained propaganda detection shared task 2019. To
address the tasks of sentence (SLC) and fragment level (FLC) propaganda
detection, we explore different neural architectures (e.g., CNN, LSTM-CRF and
BERT) and extract linguistic (e.g., part-of-speech, named entity, readability,
sentiment, emotion, etc.), layout and topical features. Specifically, we have
designed multi-granularity and multi-tasking neural architectures to jointly
perform both the sentence and fragment level propaganda detection.
Additionally, we investigate different ensemble schemes such as
majority-voting, relax-voting, etc. to boost overall system performance.
Compared to the other participating systems, our submissions are ranked 3rd and
4th in FLC and SLC tasks, respectively.
