  When humans read text, they fixate some words and skip others. However, there
have been few attempts to explain skipping behavior with computational models,
as most existing work has focused on predicting reading times (e.g.,~using
surprisal). In this paper, we propose a novel approach that models both
skipping and reading, using an unsupervised architecture that combines a neural
attention with autoencoding, trained on raw text using reinforcement learning.
Our model explains human reading behavior as a tradeoff between precision of
language understanding (encoding the input accurately) and economy of attention
(fixating as few words as possible). We evaluate the model on the Dundee
eye-tracking corpus, showing that it accurately predicts skipping behavior and
reading times, is competitive with surprisal, and captures known qualitative
features of human reading.
