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Undermining the impact of hateful content with informed and non-aggressiveresponses, called counter narratives, has emerged as a possible solution forhaving healthier online communities. Thus, some NLP studies have startedaddressing the task of counter narrative generation. Although such studies havemade an effort to build hate speech / counter narrative (HS/CN) datasets forneural generation, they fall short in reaching either high-quality and/orhigh-quantity. In this paper, we propose a novel human-in-the-loop datacollection methodology in which a generative language model is refinediteratively by using its own data from the previous loops to generate newtraining samples that experts review and/or post-edit. Our experimentscomprised several loops including dynamic variations. Results show that themethodology is scalable and facilitates diverse, novel, and cost-effective datacollection. To our knowledge, the resulting dataset is the only expert-basedmulti-target HS/CN dataset available to the community.

Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech