  In this paper, we propose DimonGen, which aims to generate diverse sentences
describing concept relationships in various everyday scenarios. To support
this, we first create a benchmark dataset for this task by adapting the
existing CommonGen dataset. We then propose a two-stage model called MoREE to
generate the target sentences. MoREE consists of a mixture of retrievers model
that retrieves diverse context sentences related to the given concepts, and a
mixture of generators model that generates diverse sentences based on the
retrieved contexts. We conduct experiments on the DimonGen task and show that
MoREE outperforms strong baselines in terms of both the quality and diversity
of the generated sentences. Our results demonstrate that MoREE is able to
generate diverse sentences that reflect different relationships between
concepts, leading to a comprehensive understanding of concept relationships.
