Web: http://arxiv.org/abs/2209.07239

Sept. 16, 2022, 1:16 a.m. | Yunyi Yang, Hong Ding, Qingyi Liu, Xiaojun Quan

cs.CL updates on arXiv.org arxiv.org

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 …

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