Towards a Unified Framework for Adaptable Problematic Content Detection
  via Continual Learning
Ali Omrani Alireza S. Ziabari Preni Golazizian Jeffrey Sorensen Morteza Dehghani
Detecting problematic content, such as hate speech, is a multifaceted and
ever-changing task, influenced by social dynamics, user populations, diversity
of sources, and evolving language. There has been significant efforts, both in
academia and in industry, to develop annotated resources that capture various
aspects of problematic content. Due to researchers' diverse objectives, the
annotations are inconsistent and hence, reports of progress on detection of
problematic content are fragmented. This pattern is expected to persist unless
we consolidate resources considering the dynamic nature of the problem. We
propose integrating the available resources, and leveraging their dynamic
nature to break this pattern. In this paper, we introduce a continual learning
benchmark and framework for problematic content detection comprising over 84
related tasks encompassing 15 annotation schemas from 8 sources. Our benchmark
creates a novel measure of progress: prioritizing the adaptability of
classifiers to evolving tasks over excelling in specific tasks. To ensure the
continuous relevance of our framework, we designed it so that new tasks can
easily be integrated into the benchmark. Our baseline results demonstrate the
potential of continual learning in capturing the evolving content and adapting
to novel manifestations of problematic content.