We present a new theoretical perspective of data noising in recurrent neural network language models (Xie et al., 2017). We show that each variant of data noising is an instance of Bayesian recurrent neural networks with a particular variational distribution (i.e., a mixture of Gaussians whose weights depend on statistics derived from the corpus such as the unigram distribution). We use this insight to propose a more principled method to apply at prediction time and propose natural extensions to data noising under the variational framework. In particular, we propose variational smoothing with tied input and output embedding matrices and an element-wise variational smoothing method. We empirically verify our analysis on two benchmark language modeling datasets and demonstrate performance improvements over existing data noising methods.

提出一种新的关于循环神经网络语言模型中数据噪声的理论视角，证明了每一种数据噪声变种都是贝叶斯循环神经网络的一个实例，我们利用这个视角来提出一种更加有原则的方法，并在变分框架下提出了自然的数据噪声扩展方法，通过两个基准的语言建模数据集的实验证明了我们分析的有效性，并证明了我们的方法比现有的数据噪声方法更具实际效果。

递归神经网络语言模型中的变分平滑