  Self-attention networks have proven to be of profound value for its strength
of capturing global dependencies. In this work, we propose to model localness
for self-attention networks, which enhances the ability of capturing useful
local context. We cast localness modeling as a learnable Gaussian bias, which
indicates the central and scope of the local region to be paid more attention.
The bias is then incorporated into the original attention distribution to form
a revised distribution. To maintain the strength of capturing long distance
dependencies and enhance the ability of capturing short-range dependencies, we
only apply localness modeling to lower layers of self-attention networks.
Quantitative and qualitative analyses on Chinese-English and English-German
translation tasks demonstrate the effectiveness and universality of the
proposed approach.
