Knowledge-grounded conversation (KGC) shows excellent potential to deliver an engaging and informative response. However, existing approaches emphasize selecting one golden knowledge given a particular dialogue context, overlooking the one-to-many phenomenon in dialogue. As a result, the existing paradigm limits the diversity of knowledge selection and generation. To this end, we establish a multi-reference KGC dataset and propose a series of metrics to systematically assess the one-to-many efficacy of existing KGC models. Furthermore, to extend the hypothesis space of knowledge selection to enhance the mapping relationship between multiple knowledge and multiple responses, we devise a span-based variational model and optimize the model in a wake-sleep style with an ameliorated evidence lower bound objective to learn the one-to-many generalization. Both automatic and human evaluations demonstrate the efficacy of our approach.

提出一种多参考知识对话模型(KGC)，为了提高知识选择与生成的多样性，建立了一个多重参考的KGC数据集，并提出了一系列测量现有KGC模型效力的指标，使用基于跨度的变分模型并以改进的证据下界目标进行优化，以学习一对多泛化关系，该方法在机器和人工评估中均表现出良好的效力。

基于知识的对话生成：对抗性多参照学习中不存在标准答案