Rule By Example: Harnessing Logical Rules for Explainable Hate Speech
  Detection
Christopher Clarke Matthew Hall Gaurav Mittal Ye Yu Sandra Sajeev Jason Mars Mei Chen
Classic approaches to content moderation typically apply a rule-based
heuristic approach to flag content. While rules are easily customizable and
intuitive for humans to interpret, they are inherently fragile and lack the
flexibility or robustness needed to moderate the vast amount of undesirable
content found online today. Recent advances in deep learning have demonstrated
the promise of using highly effective deep neural models to overcome these
challenges. However, despite the improved performance, these data-driven models
lack transparency and explainability, often leading to mistrust from everyday
users and a lack of adoption by many platforms. In this paper, we present Rule
By Example (RBE): a novel exemplar-based contrastive learning approach for
learning from logical rules for the task of textual content moderation. RBE is
capable of providing rule-grounded predictions, allowing for more explainable
and customizable predictions compared to typical deep learning-based
approaches. We demonstrate that our approach is capable of learning rich rule
embedding representations using only a few data examples. Experimental results
on 3 popular hate speech classification datasets show that RBE is able to
outperform state-of-the-art deep learning classifiers as well as the use of
rules in both supervised and unsupervised settings while providing explainable
model predictions via rule-grounding.