UBARv2: Towards Mitigating Exposure Bias in Task-Oriented Dialogs
It addresses robustness issues in multi-turn dialog systems, which is incremental as it builds on prior methods like UBAR.
The paper tackles exposure bias in task-oriented dialog systems by proposing session-level sampling and dropout-based consistency regularization, achieving state-of-the-art performance on the MultiWOZ benchmark.
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.