The potential effectiveness of counterspeech as a hate speech mitigation strategy is attracting increasing interest in the NLG research community, particularly towards the task of automatically producing it.However, automatically generated responses often lack the argumentative richness which characterises expert-produced counterspeech.In this work, we focus on two aspects of counterspeech generation to produce more cogent responses.First, by investigating the tension between helpfulness and harmlessness of LLMs, we test whether the presence of safety guardrails hinders the quality of the generations.Secondly, we assess whether attacking a specific component of the hate speech results in a more effective argumentative strategy to fight online hate.By conducting an extensive human and automatic evaluation, we show how the presence of safety guardrails can be detrimental also to a task that inherently aims at fostering positive social interactions.Moreover, our results show that attacking a specific component of the hate speech, and in particular its implicit negative stereotype and its hateful parts, leads to higher-quality generations.Content warning: this paper contains unobfuscated examples some readers may find offensive

Fighting online hate speech is a challenge that is usually addressed using Natural Language Processing via automatic detection and removal of hate content.Besides this approach, counter narratives have emerged as an effective tool employed by NGOs to respond to online hate on social media platforms.For this reason, Natural Language Generation is currently being studied as a way to automatize counter narrative writing.However, the existing resources necessary to train NLG models are limited to 2-turn interactions (a hate speech and a counter narrative as response), while in real life, interactions can consist of multiple turns.In this paper, we present a hybrid approach for dialogical data collection, which combines the intervention of human expert annotators over machine generated dialogues obtained using 19 different configurations.The result of this work is DI-ALOCONAN, the first dataset comprising over 3000 fictitious multi-turn dialogues between a hater and an NGO operator, covering 6 targets of hate.

Margherita Fanton, Helena Bonaldi, Serra Sinem Tekiroğlu, Marco Guerini. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.