  The PAN 2020 authorship verification (AV) challenge focuses on a
cross-topic/closed-set AV task over a collection of fanfiction texts.
Fanfiction is a fan-written extension of a storyline in which a so-called
fandom topic describes the principal subject of the document. The data provided
in the PAN 2020 AV task is quite challenging because authors of texts across
multiple/different fandom topics are included. In this work, we present a
hierarchical fusion of two well-known approaches into a single end-to-end
learning procedure: A deep metric learning framework at the bottom aims to
learn a pseudo-metric that maps a document of variable length onto a
fixed-sized feature vector. At the top, we incorporate a probabilistic layer to
perform Bayes factor scoring in the learned metric space. We also provide text
preprocessing strategies to deal with the cross-topic issue.
