O2D2: Out-Of-Distribution Detector to Capture Undecidable Trials in Authorship Verification

Abstract

The PAN 2021 authorship verification (AV) challenge is part of a three-year strategy, moving from a cross-topic/closed-set to a cross-topic/open-set AV task over a collection of fanfiction texts. In this work, we present our modified hybrid neural-probabilistic framework. It is based on our 2020 winning submission, with updates to significantly reduce sensitivities to topical variations and to further improve the system's calibration by means of an uncertainty-adaptation layer. Our framework additionally includes an Out-Of-Distribution Detector (O2D2) for defining non-responses, outperforming all other systems that participated in the PAN 2021 AV task.