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Pre-trained conversation models (PCMs) have achieved promising progress inrecent years. However, existing PCMs for Task-oriented dialog (TOD) areinsufficient for capturing the sequential nature of the TOD-related tasks, aswell as for learning dialog policy information. To alleviate these problems,this paper proposes a task-progressive PCM with two policy-aware pre-trainingtasks. The model is pre-trained through three stages where TOD-related tasksare progressively employed according to the task logic of the TOD system. Aglobal policy consistency task is designed to capture the multi-turn dialogpolicy sequential relation, and an act-based contrastive learning task isdesigned to capture similarities among samples with the same dialog policy. Ourmodel achieves better results on both MultiWOZ and In-Car end-to-end dialogmodeling benchmarks with only 18\% parameters and 25\% pre-training datacompared to the previous state-of-the-art PCM, GALAXY.

A Task-oriented Dialog Model with Task-progressive and Policy-aware Pre-training