  We are addressing two fundamental problems in authorship verification (AV):
Topic variability and miscalibration. Variations in the topic of two disputed
texts are a major cause of error for most AV systems. In addition, it is
observed that the underlying probability estimates produced by deep learning AV
mechanisms oftentimes do not match the actual case counts in the respective
training data. As such, probability estimates are poorly calibrated. We are
expanding our framework from PAN 2020 to include Bayes factor scoring (BFS) and
an uncertainty adaptation layer (UAL) to address both problems. Experiments
with the 2020/21 PAN AV shared task data show that the proposed method
significantly reduces sensitivities to topical variations and significantly
improves the system's calibration.
