Neural dialogue generation models trained with the one-hot target distribution suffer from the over-confidence issue, which leads to poor generation diversity as widely reported in the literature. Although existing approaches such as label smoothing can alleviate this issue, they fail to adapt to diverse dialog contexts. In this paper, we propose an Adaptive Label Smoothing (AdaLabel) approach that can adaptively estimate a target label distribution at each time step for different contexts. The maximum probability in the predicted distribution is used to modify the soft target distribution produced by a novel light-weight bi-directional decoder module. The resulting target distribution is aware of both previous and future contexts and is adjusted to avoid over-training the dialogue model. Our model can be trained in an end-to-end manner. Extensive experiments on two benchmark datasets show that our approach outperforms various competitive baselines in producing diverse responses.

该研究提出了一种自适应标签平滑（AdaLabel）方法，可以在不同的上下文环境中自适应地估计目标标签分布，以产生多样化的神经对话生成模型，该模型利用轻量级双向解码器模块产生软目标分布，避免了过度训练，实现了一种端到端的训练方式。实验结果表明，该方法在产生多样性回应方面优于其他基线模型。

通过自适应标签平滑实现对话生成的多样化