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Recently, much progress has been made in learning general-purpose sentencerepresentations that can be used across domains. However, most of the existingmodels typically treat each word in a sentence equally. In contrast, extensivestudies have proven that human read sentences efficiently by making a sequenceof fixation and saccades. This motivates us to improve sentence representationsby assigning different weights to the vectors of the component words, which canbe treated as an attention mechanism on single sentences. To that end, wepropose two novel attention models, in which the attention weights are derivedusing significant predictors of human reading time, i.e., Surprisal, POS tagsand CCG supertags. The extensive experiments demonstrate that the proposedmethods significantly improve upon the state-of-the-art sentence representationmodels.

Learning Sentence Representation with Guidance of Human Attention