  With the development of pre-trained language models, remarkable success has
been witnessed in dialogue understanding (DU). However, current DU approaches
usually employ independent models for each distinct DU task without considering
shared knowledge across different DU tasks. In this paper, we propose a unified
generative dialogue understanding framework, named {\em UniDU}, to achieve
effective information exchange across diverse DU tasks. Here, we reformulate
all DU tasks into a unified prompt-based generative model paradigm. More
importantly, a novel model-agnostic multi-task training strategy (MATS) is
introduced to dynamically adapt the weights of diverse tasks for best knowledge
sharing during training, based on the nature and available data of each task.
Experiments on ten DU datasets covering five fundamental DU tasks show that the
proposed UniDU framework largely outperforms task-specific well-designed
methods on all tasks. MATS also reveals the knowledge-sharing structure of
these tasks. Finally, UniDU obtains promising performance in the unseen
dialogue domain, showing the great potential for generalization.
