General-purpose text decoding approaches are usually adopted for dialogue response generation. Although the quality of the generated responses can be improved with dialogue-specific encoding methods, conversational decoding methods are still under-explored. Inspired by \citet{wu2023learning} that a good dialogue feature space should follow the rules of locality and isotropy, we present a fine-grained conversational decoding method, termed \textit{isotropic and proximal search (IPS)}. Our method is designed to generate the semantic-concentrated response, while still maintaining informativeness and discrimination against the context. Experiments show that our approach outperforms existing decoding strategies in the dialogue field across both automatic and human evaluation metrics. More in-depth analyses further confirm the effectiveness of our approach.

翻译：通用文本解码方法通常用于对话回复生成。尽管通过对话特定编码方法可以提升生成回复的质量，但对话解码方法仍未被充分探索。受\citet{wu2023learning}中"良好对话特征空间应遵循局部性和各向同性规则"的启发，我们提出了一种细粒度对话解码方法，称为\textit{各向同性近邻搜索(IPS)}。该方法旨在生成语义聚焦的回复，同时保持对上下文的信息性和区分性。实验表明，我们的方法在自动评估与人工评估指标上均优于现有对话领域的解码策略。更深入的分析进一步验证了该方法的有效性。