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On the State of the Art in Authorship Attribution and Authorship Verification. (arXiv:2209.06869v2 [cs.CL] UPDATED)
Oct. 6, 2022, 1:16 a.m. | Jacob Tyo, Bhuwan Dhingra, Zachary C. Lipton
cs.CL updates on arXiv.org arxiv.org
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 …
More from arxiv.org / cs.CL updates on arXiv.org
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