UniGDD: A Unified Generative Framework for Goal-Oriented Document-Grounded Dialogue

Wai Lam, Chang Gao, Wenxuan Zhang

Abstract:

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.