Recurrent neural networks (RNNs) serve as a fundamental building block for many sequence tasks across natural language processing. Recent research has focused on recurrent dropout techniques or custom RNN cells in order to improve performance. Both of these can require substantial modifications to the machine learning model or to the underlying RNN configurations. We revisit traditional regularization techniques, specifically L2 regularization on RNN activations and slowness regularization over successive hidden states, to improve the performance of RNNs on the task of language modeling. Both of these techniques require minimal modification to existing RNN architectures and result in performance improvements comparable or superior to more complicated regularization techniques or custom cell architectures. These regularization techniques can be used without any modification on optimized LSTM implementations such as the NVIDIA cuDNN LSTM.

本文通过使用传统正则化技术L2规则化和缓慢规则化来提高RNN对于语言建模任务的性能，而这两种技术只需要对现有RNN架构进行最小修改，即可获得与复杂正则化技术或自定义单元结构相媲美或更好的性能表现，并且这些技术可以在现有的优化LSTM实现上无需进行任何修改。

重新审视激活正则化对于语言循环神经网络的影响