Longer Fixations, More Computation: Gaze-Guided Recurrent Neural
  Networks
Xinting Huang Jiajing Wan Ioannis Kritikos Nora Hollenstein
Humans read texts at a varying pace, while machine learning models treat each
token in the same way in terms of a computational process. Therefore, we ask,
does it help to make models act more like humans? In this paper, we convert
this intuition into a set of novel models with fixation-guided parallel RNNs or
layers and conduct various experiments on language modeling and sentiment
analysis tasks to test their effectiveness, thus providing empirical validation
for this intuition. Our proposed models achieve good performance on the
language modeling task, considerably surpassing the baseline model. In
addition, we find that, interestingly, the fixation duration predicted by
neural networks bears some resemblance to humans' fixation. Without any
explicit guidance, the model makes similar choices to humans. We also
investigate the reasons for the differences between them, which explain why
"model fixations" are often more suitable than human fixations, when used to
guide language models.