We investigate the potential of rule-based systems for the task of offensive text
detection in English and German, and
demonstrate their effectiveness in low-resource settings, as an alternative or addition
to transfer learning across tasks and
languages. Task definitions and annotation guidelines used by existing datasets show
great variety, hence state-of-the-art
machine learning models do not transfer well across datasets or languages. Furthermore,
such systems lack explainability
and pose a critical risk of unintended bias. We present simple rule systems based
on semantic graphs for classifying offensive
text in two languages and provide both quantitative and qualitative comparison of
their performance with deep learning
models on 5 datasets across multiple languages and shared tasks.
