  Countering online hate speech is a critical yet challenging task, but one
which can be aided by the use of Natural Language Processing (NLP) techniques.
Previous research has primarily focused on the development of NLP methods to
automatically and effectively detect online hate speech while disregarding
further action needed to calm and discourage individuals from using hate speech
in the future. In addition, most existing hate speech datasets treat each post
as an isolated instance, ignoring the conversational context. In this paper, we
propose a novel task of generative hate speech intervention, where the goal is
to automatically generate responses to intervene during online conversations
that contain hate speech. As a part of this work, we introduce two
fully-labeled large-scale hate speech intervention datasets collected from Gab
and Reddit. These datasets provide conversation segments, hate speech labels,
as well as intervention responses written by Mechanical Turk Workers. In this
paper, we also analyze the datasets to understand the common intervention
strategies and explore the performance of common automatic response generation
methods on these new datasets to provide a benchmark for future research.
