  Open-domain multi-turn conversations normally face the challenges of how to
enrich and expand the content of the conversation. Recently, many approaches
based on external knowledge are proposed to generate rich semantic and
information conversation. Two types of knowledge have been studied for
knowledge-aware open-domain dialogue generation: structured triples from
knowledge graphs and unstructured texts from documents. To take both advantages
of abundant unstructured latent knowledge in the documents and the information
expansion capabilities of the structured knowledge graph, this paper presents a
new dialogue generation model, Dynamic Multi-form Knowledge Fusion based
Open-domain Chatt-ing Machine (DMKCM).In particular, DMKCM applies an indexed
text (a virtual Knowledge Base) to locate relevant documents as 1st hop and
then expands the content of the dialogue and its 1st hop using a commonsense
knowledge graph to get apposite triples as 2nd hop. To merge these two forms of
knowledge into the dialogue effectively, we design a dynamic virtual knowledge
selector and a controller that help to enrich and expand knowledge space.
Moreover, DMKCM adopts a novel dynamic knowledge memory module that effectively
uses historical reasoning knowledge to generate better responses. Experimental
results indicate the effectiveness of our method in terms of dialogue coherence
and informativeness.
