Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution

Benedikt Boenninghoff, Steffen Zeiler, Robert Nickel, Dorothea Kolossa


Abstract
Traditional computational authorship attribution describes a classification task in a closed-set scenario. Given a finite set of candidate authors and corresponding labeled texts, the objective is to determine which of the authors has written another set of anonymous or disputed texts. In this work, we propose a probabilistic autoencoding framework to deal with this supervised classification task. Variational autoencoders (VAEs) have had tremendous success in learning latent representations. However, existing VAEs are currently still bound by limitations imposed by the assumed Gaussianity of the underlying probability distributions in the latent space. In this work, we are extending a VAE with an embedded Gaussian mixture model to a Student-t mixture model, which allows for an independent control of the “heaviness” of the respective tails of the implied probability densities. Experiments over an Amazon review dataset indicate superior performance of the proposed method.
Anthology ID:
2020.coling-main.45
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
519–529
Language:
URL:
https://aclanthology.org/2020.coling-main.45
DOI:
10.18653/v1/2020.coling-main.45
Bibkey:
Cite (ACL):
Benedikt Boenninghoff, Steffen Zeiler, Robert Nickel, and Dorothea Kolossa. 2020. Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution. In Proceedings of the 28th International Conference on Computational Linguistics, pages 519–529, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution (Boenninghoff et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.45.pdf