  The goal-oriented document-grounded dialogue aims at responding to the user
query based on the dialogue context and supporting document. Existing studies
tackle this problem by decomposing it into two sub-tasks: knowledge
identification and response generation. However, such pipeline methods would
unavoidably suffer from the error propagation issue. This paper proposes to
unify these two sub-tasks via sequentially generating the grounding knowledge
and the response. We further develop a prompt-connected multi-task learning
strategy to model the characteristics and connections of different tasks and
introduce linear temperature scheduling to reduce the negative effect of
irrelevant document information. Experimental results demonstrate the
effectiveness of our framework.
