Pre-trained language models have been recently shown to benefit task-oriented
dialogue (TOD) systems. Despite their success, existing methods often formulate
this task as a cascaded generation problem which can lead to error accumulation
across different sub-tasks and greater data annotation overhead. In this study,
we present PPTOD, a unified plug-and-play model for task-oriented dialogue. In
addition, we introduce a new dialogue multi-task pre-training strategy that
allows the model to learn the primary TOD task completion skills from
heterogeneous dialog corpora. We extensively test our model on three benchmark
TOD tasks, including end-to-end dialogue modelling, dialogue state tracking,
and intent classification. Experimental results show that PPTOD achieves new
state of the art on all evaluated tasks in both high-resource and low-resource
scenarios. Furthermore, comparisons against previous SOTA methods show that the
responses generated by PPTOD are more factually correct and semantically
coherent as judged by human annotators.

该研究提出了通过 PPTOD 模型和新的对话多任务预训练策略来解决任务导向对话系统中存在的级联生成问题，取得了针对几个基准 TOD 任务的最新成果。