Modeling Localness for Self-Attention Networks
Baosong Yang, Zhaopeng Tu, Derek F. Wong, Fandong Meng, Lidia Sam Chao, Tong Zhang · 2018
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.