In view of the
problem that the existing multi-party human-computer dialogue system is prone to
ignore speakers who may be forgotten during the dialogue process, leading to
weak user interaction initiative and poor user dialogue experience,a multi-party
human-computer active dialogue strategy based on knowledge enhancement is
proposed. The strategy uses a knowledge graph as external knowledge to learn
the preferences of specific individuals who may be overlooked in the dialogue,
combine the knowledge with the current group dialogue requirements to design a
personalized response strategy, thereby encouraging individual active
participation in group conversations. Initially, a graph attention mechanism is
employed to learn the representation of interest entities specific to
individuals, and a deep preference representation for individuals is obtained
by introducing time-weighted aggregation. Subsequently, multiple neighbor sets
are triggered along the path of the current dialogue topic knowledge subgraph
to actively capture the high-level personalized interest topics of individuals.
Finally, through semantic-level analysis of the current group dialogue context
and requirements, the satisfaction of the group with candidate topics is
evaluated, and the optimal dialogue topic is generated, which both satisfy individual
preferences and consider group sentiments. Experimental results indicate that a
multi-party human-computer dialogue system, which integrates external knowledge
and focuses on specific speakers, effectively enhances the richness of response
content and interaction satisfaction from participants, fostering continuous
multi-party dialogue.
