Counterspeeches up my sleeve! Intent Distribution Learning and
  Persistent Fusion for Intent-Conditioned Counterspeech Generation
Rishabh Gupta Shaily Desai Manvi Goel Anil Bandhakavi Tanmoy Chakraborty Md. Shad Akhtar
Counterspeech has been demonstrated to be an efficacious approach for
combating hate speech. While various conventional and controlled approaches
have been studied in recent years to generate counterspeech, a counterspeech
with a certain intent may not be sufficient in every scenario. Due to the
complex and multifaceted nature of hate speech, utilizing multiple forms of
counter-narratives with varying intents may be advantageous in different
circumstances. In this paper, we explore intent-conditioned counterspeech
generation. At first, we develop IntentCONAN, a diversified intent-specific
counterspeech dataset with 6831 counterspeeches conditioned on five intents,
i.e., informative, denouncing, question, positive, and humour. Subsequently, we
propose QUARC, a two-stage framework for intent-conditioned counterspeech
generation. QUARC leverages vector-quantized representations learned for each
intent category along with PerFuMe, a novel fusion module to incorporate
intent-specific information into the model. Our evaluation demonstrates that
QUARC outperforms several baselines by an average of 10% across evaluation
metrics. An extensive human evaluation supplements our hypothesis of better and
more appropriate responses than comparative systems.