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.

翻译：PAN 2021 作者核查(AV)是一个三年期战略的一部分,从跨主题/闭门设置的AV任务转向对一系列影视文本的跨主题/开放的AV任务。在这项工作中,我们提出了一个新颖的混合神经概率框架,旨在应对2021年任务的挑战。我们的系统以我们2020年的中选提交为基础,更新的目的是大大减少对时事变异的敏感度,并通过不确定性适应层进一步改进系统的校准。我们的框架还包括一个用于界定不回应的分布探测器(O2D2)。我们提议的系统优于参加PAN 2021 AV任务的所有其他系统。