Knowledge selection is the key in knowledge-grounded dialogues (KGD), which
aims to select an appropriate knowledge snippet to be used in the utterance
based on dialogue history. Previous studies mainly employ the classification
approach to classify each candidate snippet as "relevant" or "irrelevant"
independently. However, such approaches neglect the interactions between
snippets, leading to difficulties in inferring the meaning of snippets.
Moreover, they lack modeling of the discourse structure of dialogue-knowledge
interactions. We propose a simple yet effective generative approach for
knowledge selection, called GenKS. GenKS learns to select snippets by
generating their identifiers with a sequence-to-sequence model. GenKS therefore
captures intra-knowledge interaction inherently through attention mechanisms.
Meanwhile, we devise a hyperlink mechanism to model the dialogue-knowledge
interactions explicitly. We conduct experiments on three benchmark datasets,
and verify GenKS achieves the best results on both knowledge selection and
response generation.

本文提出了一种生成式方法，名为 GenKS，用于在基于对话历史的语境下选择适当的知识片段，该方法通过序列到序列模型生成标识符来选择片段，捕捉片段之间的内部知识交互，并通过超链接机制显式地建模对话 - 知识交互，同时在三个基准数据集上进行实验并证明 GenKS 在知识选择和响应生成方面取得了最佳结果。