  Persuasion techniques detection in news in a multi-lingual setup is
non-trivial and comes with challenges, including little training data. Our
system successfully leverages (back-)translation as data augmentation
strategies with multi-lingual transformer models for the task of detecting
persuasion techniques. The automatic and human evaluation of our augmented data
allows us to explore whether (back-)translation aid or hinder performance. Our
in-depth analyses indicate that both data augmentation strategies boost
performance; however, balancing human-produced and machine-generated data seems
to be crucial.
