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Modeling Human Reading with Neural Attention

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Figure

Figure 1: The architecture of the proposed model, reading a three-word input sequencethe probability distribution it computes in each time step.according to w1,w2,w3
Table 2: Evaluation of fixation sequence predictions against hu-man data. For the human baseline, we predicted the n-th reader’sfixations by taking the fixations of the n + 1-th reader (withmissing values replaced by reader average), averaging the re-sulting scores over the ten readers.
Table 1: Performance on language modeling and reconstruction as measured by perplexity
Table 3: Linear mixed effects models for first pass duration.The first part of the table shows the coefficients, standard er-rors, and t values for the predictors in the baseline model
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