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.
More recently, Sukhbaatar et al. REF add a learnable self-attention span to extend the size of the context.
Adaptive Attention Span in Transformers
ArXiv
ArXiv
Mathematics Computer Science