Adaptive Attention Span In Transformers

Sukhbaatar Sainbayar, Grave Edouard, Bojanowski Piotr, Joulin Armand. Arxiv 2019

[Paper]    
Attention Mechanism Language Modeling Model Architecture Pretraining Methods Transformer

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.

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