Deep Bayes Factor Scoring for Authorship Verification

• URL: http://arxiv.org/abs/2008.10105v1
• Date: Sun, 23 Aug 2020 21:00:33 GMT
• Title: Deep Bayes Factor Scoring for Authorship Verification
• Authors: Benedikt Boenninghoff and Julian Rupp and Robert M. Nickel and Dorothea Kolossa
• Abstract summary: 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.
• Score: 10.405174977499497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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