O2D2: Out-Of-Distribution Detector to Capture Undecidable Trials in Authorship Verification
This work addresses authorship verification in fanfiction texts, but it is incremental as it builds on a previous winning submission.
The authors tackled the PAN 2021 authorship verification challenge by developing a hybrid neural-probabilistic framework with an out-of-distribution detector, which outperformed all other systems in the task.
The PAN 2021 authorship verification (AV) challenge is part of a three-year strategy, moving from a cross-topic/closed-set AV task to a cross-topic/open-set AV task over a collection of fanfiction texts. In this work, we present a novel hybrid neural-probabilistic framework that is designed to tackle the challenges of the 2021 task. Our system 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. Our proposed system outperformed all other systems that participated in the PAN 2021 AV task.