  A novel solution to span detection and classification is presented in which a
BART EncoderDecoder model is used to transform textual input into a version
with XML-like marked up spans. This markup is subsequently translated to an
identification of the beginning and end of fragments and of their classes.
Discussed is how pre-training methodology both explains the relative success of
this method and its limitations. This paper reports on participation in task 6
of SemEval-2021: Detection of Persuasion Techniques in Texts and Images.
