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

A Benchmark Dataset for Learning to Intervene in Online Hate Speech