Gates Are Not What You Need in RNNs
This work addresses the need for simpler and more robust RNN architectures for practitioners in machine learning, though it appears incremental as it builds on existing concepts like residual connections.
The paper tackles the problem of gate redundancy in recurrent neural networks by proposing a new gating-free recurrent cell called Residual Recurrent Unit (RRU), which outperforms traditional gated units like GRU and LSTM on tasks such as polyphonic music modeling, language modeling, and sentiment analysis.
Recurrent neural networks have flourished in many areas. Consequently, we can see new RNN cells being developed continuously, usually by creating or using gates in a new, original way. But what if we told you that gates in RNNs are redundant? In this paper, we propose a new recurrent cell called Residual Recurrent Unit (RRU) which beats traditional cells and does not employ a single gate. It is based on the residual shortcut connection, linear transformations, ReLU, and normalization. To evaluate our cell's effectiveness, we compare its performance against the widely-used GRU and LSTM cells and the recently proposed Mogrifier LSTM on several tasks including, polyphonic music modeling, language modeling, and sentiment analysis. Our experiments show that RRU outperforms the traditional gated units on most of these tasks. Also, it has better robustness to parameter selection, allowing immediate application in new tasks without much tuning. We have implemented the RRU in TensorFlow, and the code is made available at https://github.com/LUMII-Syslab/RRU .