  Pre-trained language models have been successful in many scenarios. However,
their usefulness in task-oriented dialogues is limited due to the intrinsic
linguistic differences between general text and task-oriented dialogues.
Current task-oriented dialogue pre-training methods rely on a contrastive
framework, which faces challenges such as selecting true positives and hard
negatives, as well as lacking diversity. In this paper, we propose a novel
dialogue pre-training model called BootTOD. It learns task-oriented dialogue
representations via a self-bootstrapping framework. Unlike contrastive
counterparts, BootTOD aligns context and context+response representations and
dismisses the requirements of contrastive pairs. BootTOD also uses multiple
appropriate response targets to model the intrinsic one-to-many diversity of
human conversations. Experimental results show that BootTOD outperforms strong
TOD baselines on diverse downstream dialogue tasks.
