  We propose a novel self-attention mechanism that can learn its optimal
attention span. This allows us to extend significantly the maximum context size
used in Transformer, while maintaining control over their memory footprint and
computational time. We show the effectiveness of our approach on the task of
character level language modeling, where we achieve state-of-the-art
performances on text8 and enwiki8 by using a maximum context of 8k characters.
