Computer Science > Computation and Language
[Submitted on 14 Sep 2022 (v1), last revised 5 Oct 2022 (this version, v2)]
Title:On the State of the Art in Authorship Attribution and Authorship Verification
View PDFAbstract:Despite decades of research on authorship attribution (AA) and authorship verification (AV), inconsistent dataset splits/filtering and mismatched evaluation methods make it difficult to assess the state of the art. In this paper, we present a survey of the fields, resolve points of confusion, introduce Valla that standardizes and benchmarks AA/AV datasets and metrics, provide a large-scale empirical evaluation, and provide apples-to-apples comparisons between existing methods. We evaluate eight promising methods on fifteen datasets (including distribution-shifted challenge sets) and introduce a new large-scale dataset based on texts archived by Project Gutenberg. Surprisingly, we find that a traditional Ngram-based model performs best on 5 (of 7) AA tasks, achieving an average macro-accuracy of $76.50\%$ (compared to $66.71\%$ for a BERT-based model). However, on the two AA datasets with the greatest number of words per author, as well as on the AV datasets, BERT-based models perform best. While AV methods are easily applied to AA, they are seldom included as baselines in AA papers. We show that through the application of hard-negative mining, AV methods are competitive alternatives to AA methods. Valla and all experiment code can be found here: this https URL
Submission history
From: Jacob Tyo [view email][v1] Wed, 14 Sep 2022 18:32:26 UTC (57 KB)
[v2] Wed, 5 Oct 2022 14:05:15 UTC (52 KB)
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