  Making chatbots world aware in a conversation like a human is a crucial
challenge, where the world may contain dynamic knowledge and spatiotemporal
state. Several recent advances have tried to link the dialog system to a static
knowledge base or search engine, but they do not contain all the world
information needed for conversations. In contrast, we propose a new method to
improve the dialogue system using spatiotemporal aware dynamic knowledge. We
utilize service information as a way for the dialogue system to link the world.
The system actively builds a request according to the dialog context and
spatiotemporal state to get service information and then generates world aware
responses. To implement this method, we collect DuSinc, an open-domain
human-human dialogue dataset, where a participant can access the service to get
the information needed for dialogue responses. Through automatic and human
evaluations, we found that service information significantly improves the
consistency, informativeness, factuality, and engagingness of the dialogue
system, making it behave more like a human. Compared to the pre-trained models
without spatiotemporal aware dynamic knowledge, the overall session-level score
was improved by 60.87\%. The collection dataset and methods will be
open-sourced.
