  Deep anomaly detection methods have become increasingly popular in recent
years, with methods like Stacked Autoencoders, Variational Autoencoders, and
Generative Adversarial Networks greatly improving the state-of-the-art. Other
methods rely on augmenting classical models (such as the One-Class Support
Vector Machine), by learning an appropriate kernel function using Neural
Networks. Recent developments in representation learning by self-supervision
are proving to be very beneficial in the context of anomaly detection. Inspired
by the advancements in anomaly detection using self-supervised learning in the
field of computer vision, this thesis aims to develop a method for detecting
anomalies by exploiting pretext tasks tailored for text corpora. This approach
greatly improves the state-of-the-art on two datasets, 20Newsgroups, and AG
News, for both semi-supervised and unsupervised anomaly detection, thus proving
the potential for self-supervised anomaly detectors in the field of natural
language processing.
