  Multi-document grounded dialogue systems (DGDS) belong to a class of
conversational agents that answer users' requests by finding supporting
knowledge from a collection of documents. Most previous studies aim to improve
the knowledge retrieval model or propose more effective ways to incorporate
external knowledge into a parametric generation model. These methods, however,
focus on retrieving knowledge from mono-granularity language units (e.g.
passages, sentences, or spans in documents), which is not enough to effectively
and efficiently capture precise knowledge in long documents. This paper
proposes Re3G, which aims to optimize both coarse-grained knowledge retrieval
and fine-grained knowledge extraction in a unified framework. Specifically, the
former efficiently finds relevant passages in a retrieval-and-reranking
process, whereas the latter effectively extracts finer-grain spans within those
passages to incorporate into a parametric answer generation model (BART, T5).
Experiments on DialDoc Shared Task demonstrate the effectiveness of our method.
