  Despite the recent successes of transformer-based models in terms of
effectiveness on a variety of tasks, their decisions often remain opaque to
humans. Explanations are particularly important for tasks like offensive
language or toxicity detection on social media because a manual appeal process
is often in place to dispute automatically flagged content. In this work, we
propose a technique to improve the interpretability of these models, based on a
simple and powerful assumption: a post is at least as toxic as its most toxic
span. We incorporate this assumption into transformer models by scoring a post
based on the maximum toxicity of its spans and augmenting the training process
to identify correct spans. We find this approach effective and can produce
explanations that exceed the quality of those provided by Logistic Regression
analysis (often regarded as a highly-interpretable model), according to a human
study.
