How to Solve Few-Shot Abusive Content Detection Using the Data We
  Actually Have
Viktor Hangya Alexander Fraser
Due to the broad range of social media platforms and their user groups, the
requirements of abusive language detection systems are varied and
ever-changing. Already a large set of annotated corpora with different
properties and label sets were created, such as hate or misogyny detection, but
the form and targets of abusive speech are constantly changing. Since, the
annotation of new corpora is expensive, in this work we leverage datasets we
already have, covering a wide range of tasks related to abusive language
detection, in order to build models cheaply for a new target label set and/or
language, using only a few training examples of the target domain. We propose a
two-step approach: first we train our model in a multitask fashion. We then
carry out few-shot adaptation to the target requirements. Our experiments show
that by leveraging already existing datasets and only a few-shots of the target
task the performance of models can be improved not only monolingually but
across languages as well. Our analysis also shows that our models acquire a
general understanding of abusive language, since they improve the prediction of
labels which are present only in the target dataset. We also analyze the
trade-off between specializing the already existing datasets to a given target
setup for best performance and its negative effects on model adaptability.