  Existing attention mechanisms are trained to attend to individual items in a
collection (the memory) with a predefined, fixed granularity, e.g., a word
token or an image grid. We propose area attention: a way to attend to areas in
the memory, where each area contains a group of items that are structurally
adjacent, e.g., spatially for a 2D memory such as images, or temporally for a
1D memory such as natural language sentences. Importantly, the shape and the
size of an area are dynamically determined via learning, which enables a model
to attend to information with varying granularity. Area attention can easily
work with existing model architectures such as multi-head attention for
simultaneously attending to multiple areas in the memory. We evaluate area
attention on two tasks: neural machine translation (both character and
token-level) and image captioning, and improve upon strong (state-of-the-art)
baselines in all the cases. These improvements are obtainable with a basic form
of area attention that is parameter free.
