  This paper studies the exposure bias problem in task-oriented dialog systems,
where the model's generated content over multiple turns drives the dialog
context away from the ground-truth distribution at training time, introducing
error propagation and damaging the robustness of the TOD system. To bridge the
gap between training and inference for multi-turn task-oriented dialogs, we
propose session-level sampling which explicitly exposes the model to sampled
generated content of dialog context during training. Additionally, we employ a
dropout-based consistency regularization with the masking strategy R-Mask to
further improve the robustness and performance of the model. The proposed
UBARv2 achieves state-of-the-art performance on the standardized evaluation
benchmark MultiWOZ and extensive experiments show the effectiveness of the
proposed methods.
