Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech
This work addresses the need for scalable counterspeech generation to combat hate speech online without restricting free speech, representing an incremental improvement over existing NLG methods.
The paper tackled the problem of generating diverse and relevant counterspeech against online hate speech, proposing a three-module pipeline that improved diversity and relevance, with experiments on three datasets demonstrating its efficacy.
Countermeasures to effectively fight the ever increasing hate speech online without blocking freedom of speech is of great social interest. Natural Language Generation (NLG), is uniquely capable of developing scalable solutions. However, off-the-shelf NLG methods are primarily sequence-to-sequence neural models and they are limited in that they generate commonplace, repetitive and safe responses regardless of the hate speech (e.g., "Please refrain from using such language.") or irrelevant responses, making them ineffective for de-escalating hateful conversations. In this paper, we design a three-module pipeline approach to effectively improve the diversity and relevance. Our proposed pipeline first generates various counterspeech candidates by a generative model to promote diversity, then filters the ungrammatical ones using a BERT model, and finally selects the most relevant counterspeech response using a novel retrieval-based method. Extensive Experiments on three representative datasets demonstrate the efficacy of our approach in generating diverse and relevant counterspeech.