  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.
