  The spread of propaganda through the internet has increased drastically over
the past years. Lately, propaganda detection has started gaining importance
because of the negative impact it has on society. In this work, we describe our
approach for the WANLP 2022 shared task which handles the task of propaganda
detection in a multi-label setting. The task demands the model to label the
given text as having one or more types of propaganda techniques. There are a
total of 21 propaganda techniques to be detected. We show that an ensemble of
five models performs the best on the task, scoring a micro-F1 score of 59.73%.
We also conduct comprehensive ablations and propose various future directions
for this work.
