  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.
