  We present our system for the two subtasks of the shared task on propaganda
detection in Arabic, part of WANLP'2022. Subtask 1 is a multi-label
classification problem to find the propaganda techniques used in a given tweet.
Our system for this task uses XLM-R to predict probabilities for the target
tweet to use each of the techniques. In addition to finding the techniques,
Subtask 2 further asks to identify the textual span for each instance of each
technique that is present in the tweet; the task can be modeled as a sequence
tagging problem. We use a multi-granularity network with mBERT encoder for
Subtask 2. Overall, our system ranks second for both subtasks (out of 14 and 3
participants, respectively). Our empirical analysis show that it does not help
to use a much larger English corpus annotated with propaganda techniques,
regardless of whether used in English or after translation to Arabic.
