# Deep Bayes Factor Scoring for Authorship Verification

arXiv: https://arxiv.org/abs/2008.10105 · HTML: https://mlsanity.com/paper/2008.10105/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2020.

## Built on by (most cited first, 7 of 7)

- [On the State of the Art in Authorship Attribution and Authorship Verification](https://mlsanity.com/paper/2209.06869/) (2022)
- [Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework](https://mlsanity.com/paper/2109.04817/) (2021)
- [VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web](https://mlsanity.com/paper/2207.03477/) (2022)
- [O2D2: Out-Of-Distribution Detector to Capture Undecidable Trials in Authorship Verification](https://mlsanity.com/paper/2106.15825/) (2021)
- [Self-Calibrating Neural-Probabilistic Model for Authorship Verification Under Covariate Shift](https://mlsanity.com/paper/2106.11196/) (2021)
- [Authorship identification under domain shift: a survey of stylistic measures and learned author representations](https://mlsanity.com/paper/2310.00436/) (2023)
- [Fusing Stylometric and Embedding Systems to Estimate Authorship Likelihood Ratios in Japanese](https://mlsanity.com/paper/2606.13991/) (2026)

## Abstract (arXiv)

The PAN 2020 authorship verification (AV) challenge focuses on a cross-topic/closed-set AV task over a collection of fanfiction texts. Fanfiction is a fan-written extension of a storyline in which a so-called fandom topic describes the principal subject of the document. The data provided in the PAN 2020 AV task is quite challenging because authors of texts across multiple/different fandom topics are included. In this work, we present a hierarchical fusion of two well-known approaches into a single end-to-end learning procedure: A deep metric learning framework at the bottom aims to learn a pseudo-metric that maps a document of variable length onto a fixed-sized feature vector. At the top, we incorporate a probabilistic layer to perform Bayes factor scoring in the learned metric space. We also provide text preprocessing strategies to deal with the cross-topic issue.