  Attention is a commonly used mechanism in sequence processing, but it is of
O(n^2) complexity which prevents its application to long sequences. The
recently introduced neural Shuffle-Exchange network offers a
computation-efficient alternative, enabling the modelling of long-range
dependencies in O(n log n) time. The model, however, is quite complex,
involving a sophisticated gating mechanism derived from the Gated Recurrent
Unit. In this paper, we present a simple and lightweight variant of the
Shuffle-Exchange network, which is based on a residual network employing GELU
and Layer Normalization. The proposed architecture not only scales to longer
sequences but also converges faster and provides better accuracy. It surpasses
the Shuffle-Exchange network on the LAMBADA language modelling task and
achieves state-of-the-art performance on the MusicNet dataset for music
transcription while being efficient in the number of parameters. We show how to
combine the improved Shuffle-Exchange network with convolutional layers,
establishing it as a useful building block in long sequence processing
applications.
