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.

基于对比最优传输的新型框架有效地解决了生成对抗性叙述的目标互动和多样化方面的挑战，通过引入 Optimal Transport Kernel (OTK) 模块将仇恨目标信息融合到标记表示中，使用自对比学习模块生成各向异性分布的标记表示，通过以令牌相似性和目标相关性为考虑因素的改进解码策略来明确促进领域相关性和多样化，从而显著优于现有方法的量化和定性实验。