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Bias Analysis and Mitigation in the Evaluation of Authorship Verification

机译：作者校正作者校正分析与缓解

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摘要

The PAN series of shared tasks is well known for its continuous and high quality research in the field of digital text forensics. Among others, PAN contributions include original corpora, tailored benchmarks, and standardized experimentation platforms. In this paper we review, theoretically and practically, the authorship verification task and conclude that the underlying experiment design cannot guarantee pushing forward the state of the art—in fact, it allows for top benchmarking with a surprisingly straightforward approach. In this regard, we present a "Basic and Fairly Flawed" (BAFF) authorship verifier that is on a par with the best approaches submitted so far, and that illustrates sources of bias that should be eliminated. We pinpoint these sources in the evaluation chain and present a refined authorship corpus as effective countermeasure.
机译：PAN系列共享任务是其在数字文本取证领域的连续和高质量的研究众所周知。除此之外，PAN贡献包括原始的Corpora，量身定制的基准和标准化的实验平台。在本文中，我们在理论上和实际地审查了作者核查任务并得出结论，潜在的实验设计不能保证推动最新的技术 - 实际上，它允许以令人惊讶的直接方法进行顶级基准。在这方面，我们提出了一个“基本和相当有缺陷的”（BAFF）作者验证者，该验证者与迄今为止提交的最佳方法相同，并说明了应该消除的偏见来源。我们在评估链中查明这些来源，并提出了一份精炼作者语料库，作为有效的对策。

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