  Attention is an important mechanism that can be employed for a variety of
deep learning models across many different domains and tasks. This survey
provides an overview of the most important attention mechanisms proposed in the
literature. The various attention mechanisms are explained by means of a
framework consisting of a general attention model, uniform notation, and a
comprehensive taxonomy of attention mechanisms. Furthermore, the various
measures for evaluating attention models are reviewed, and methods to
characterize the structure of attention models based on the proposed framework
are discussed. Last, future work in the field of attention models is
considered.
