  We propose an unsupervised solution to the Authorship Verification task that
utilizes pre-trained deep language models to compute a new metric called
DV-Distance. The proposed metric is a measure of the difference between the two
authors comparing against pre-trained language models. Our design addresses the
problem of non-comparability in authorship verification, frequently encountered
in small or cross-domain corpora. To the best of our knowledge, this paper is
the first one to introduce a method designed with non-comparability in mind
from the ground up, rather than indirectly. It is also one of the first to use
Deep Language Models in this setting. The approach is intuitive, and it is easy
to understand and interpret through visualization. Experiments on four datasets
show our methods matching or surpassing current state-of-the-art and strong
baselines in most tasks.
