  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 .
