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arXiv · 2209.07239

UBARv2: Towards Mitigating Exposure Bias in Task-Oriented Dialogs

Yunyi Yang·Hong Ding·Qingyi Liu·Xiaojun Quan
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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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Yunyi Yang, Hong Ding, Qingyi Liu, Xiaojun Quan. 2022-09-15. UBARv2: Towards Mitigating Exposure Bias in Task-Oriented Dialogs. https://arxiv.org/abs/2209.07239

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