UBARv2: Towards Mitigating Exposure Bias in Task-Oriented Dialogs
- URL: http://arxiv.org/abs/2209.07239v1
- Date: Thu, 15 Sep 2022 12:14:46 GMT
- Title: UBARv2: Towards Mitigating Exposure Bias in Task-Oriented Dialogs
- Authors: Yunyi Yang, Hong Ding, Qingyi Liu, Xiaojun Quan
- Abstract summary: We propose session-level sampling which explicitly exposes the model to sampled generated content of dialog context during training.
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.
- Score: 28.051423938045843
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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