Diversifying Dialog Generation via Adaptive Label Smoothing

Understand

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.