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 shortrange 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.
Yang et al. propose to model localness for self-attention which would be conducive to capturing local information by learning a Gaussian bias predicting the region of local attention REF .
Modeling Localness for Self-Attention Networks
ArXiv
ArXiv
Computer Science