  In this paper, we describe our system used in the shared task for
fine-grained propaganda analysis at sentence level. Despite the challenging
nature of the task, our pretrained BERT model (team YMJA) fine tuned on the
training dataset provided by the shared task scored 0.62 F1 on the test set and
ranked third among 25 teams who participated in the contest. We present a set
of illustrative experiments to better understand the performance of our BERT
model on this shared task. Further, we explore beyond the given dataset for
false-positive cases that likely to be produced by our system. We show that
despite the high performance on the given testset, our system may have the
tendency of classifying opinion pieces as propaganda and cannot distinguish
quotations of propaganda speech from actual usage of propaganda techniques.
