  Counter-narratives, which are direct responses consisting of non-aggressive
fact-based arguments, have emerged as a highly effective approach to combat the
proliferation of hate speech. Previous methodologies have primarily focused on
fine-tuning and post-editing techniques to ensure the fluency of generated
contents, while overlooking the critical aspects of individualization and
relevance concerning the specific hatred targets, such as LGBT groups,
immigrants, etc. This research paper introduces a novel framework based on
contrastive optimal transport, which effectively addresses the challenges of
maintaining target interaction and promoting diversification in generating
counter-narratives. Firstly, an Optimal Transport Kernel (OTK) module is
leveraged to incorporate hatred target information in the token
representations, in which the comparison pairs are extracted between original
and transported features. Secondly, a self-contrastive learning module is
employed to address the issue of model degeneration. This module achieves this
by generating an anisotropic distribution of token representations. Finally, a
target-oriented search method is integrated as an improved decoding strategy to
explicitly promote domain relevance and diversification in the inference
process. This strategy modifies the model's confidence score by considering
both token similarity and target relevance. Quantitative and qualitative
experiments have been evaluated on two benchmark datasets, which demonstrate
that our proposed model significantly outperforms current methods evaluated by
metrics from multiple aspects.
