Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate Speech

TL;DR

This paper introduces a three-module pipeline combining generation, filtering, and selection to produce diverse, relevant counterspeech responses to online hate speech, improving over standard NLG methods.

Contribution

The paper proposes a novel pipeline that enhances counterspeech generation by integrating a generative model, a BERT-based filter, and a retrieval-based selector, addressing diversity and relevance issues.

Findings

Improved diversity and relevance in counterspeech responses.

Effective filtering of ungrammatical responses using BERT.

Demonstrated superiority on three datasets.

Abstract

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.