  To proactively offer social media users a safe online experience, there is a
need for systems that can detect harmful posts and promptly alert platform
moderators. In order to guarantee the enforcement of a consistent policy,
moderators are provided with detailed guidelines. In contrast, most
state-of-the-art models learn what abuse is from labelled examples and as a
result base their predictions on spurious cues, such as the presence of group
identifiers, which can be unreliable. In this work we introduce the concept of
policy-aware abuse detection, abandoning the unrealistic expectation that
systems can reliably learn which phenomena constitute abuse from inspecting the
data alone. We propose a machine-friendly representation of the policy that
moderators wish to enforce, by breaking it down into a collection of intents
and slots. We collect and annotate a dataset of 3,535 English posts with such
slots, and show how architectures for intent classification and slot filling
can be used for abuse detection, while providing a rationale for model
decisions.
