Revisiting Activation Regularization for Language RNNs
This work addresses the need for simpler, effective regularization in RNNs for natural language processing, offering an incremental improvement over existing techniques.
The paper tackled the problem of improving recurrent neural networks (RNNs) for language modeling by revisiting traditional regularization techniques like L2 regularization on activations and slowness regularization, achieving performance improvements comparable or superior to more complex methods with minimal architectural changes.
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.