  State-of-the-art approaches for hate-speech detection usually exhibit poor
performance in out-of-domain settings. This occurs, typically, due to
classifiers overemphasizing source-specific information that negatively impacts
its domain invariance. Prior work has attempted to penalize terms related to
hate-speech from manually curated lists using feature attribution methods,
which quantify the importance assigned to input terms by the classifier when
making a prediction. We, instead, propose a domain adaptation approach that
automatically extracts and penalizes source-specific terms using a domain
classifier, which learns to differentiate between domains, and
feature-attribution scores for hate-speech classes, yielding consistent
improvements in cross-domain evaluation.
