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.

翻译：我们为作者核查任务提出了一个不受监督的解决方案,即使用经过预先训练的深语言模型来计算称为DV-差异值的新指标。拟议指标是衡量两位作者与经过训练的语言模型之间差异的尺度。我们的设计解决了在小型或跨领域公司中经常遇到的作者核查无可比性问题。据我们所知,本文件是第一个采用一种从地面上而不是间接地以非可比性为思想设计的方法。它也是第一个在这个环境中使用深语言模型的方法之一。这种方法直观的,很容易通过可视化来理解和解释。对四个数据集的实验显示了我们在多数任务中匹配或超过当前最先进和强的基线的方法。