Acting against online hate speech is an important challenge nowadays. Previous research has specifically focused on the development of NLP methods to automatically detect online hate speech while disregarding further action needed to mitigate hate speech in the future. This paper proposes a system that generates responses to intervene during online conversations with hate speech content. Prior to generation, the system uses a binomial, recurrent network-based classifier with a combination of word and sub-word embeddings to detect hate speech. With this architecture we achieved a Fl score of 0.786. The chatbot is based on a generative approach that uses a pre-trained transformer model and dynamically modifies the history or the persona profile to counteract the user's hate speech. This adaptation provides sentences that the system could use to respond in the presence of aggression and discrimination behaviors.
展开▼
