  The massive spread of hate speech, hateful content targeted at specific
subpopulations, is a problem of critical social importance. Automated methods
of hate speech detection typically employ state-of-the-art deep learning
(DL)-based text classifiers-large pretrained neural language models of over 100
million parameters, adapting these models to the task of hate speech detection
using relevant labeled datasets. Unfortunately, there are only a few public
labeled datasets of limited size that are available for this purpose. We make
several contributions with high potential for advancing this state of affairs.
We present HyperNetworks for hate speech detection, a special class of DL
networks whose weights are regulated by a small-scale auxiliary network. These
architectures operate at character-level, as opposed to word or subword-level,
and are several orders of magnitude smaller compared to the popular DL
classifiers. We further show that training hate detection classifiers using
additional large amounts of automatically generated examples is beneficial in
general, yet this practice especially boosts the performance of the proposed
HyperNetworks. We report the results of extensive experiments, assessing the
performance of multiple neural architectures on hate detection using five
public datasets. The assessed methods include the pretrained language models of
BERT, RoBERTa, ALBERT, MobileBERT and CharBERT, a variant of BERT that
incorporates character alongside subword embeddings. In addition to the
traditional setup of within-dataset evaluation, we perform cross-dataset
evaluation experiments, testing the generalization of the various models in
conditions of data shift. Our results show that the proposed HyperNetworks
achieve performance that is competitive, and better in some cases, than these
pretrained language models, while being smaller by orders of magnitude.
