  In this work, we analyze how human gaze during reading comprehension is
conditioned on the given reading comprehension question, and whether this
signal can be beneficial for machine reading comprehension. To this end, we
collect a new eye-tracking dataset with a large number of participants engaging
in a multiple choice reading comprehension task. Our analysis of this data
reveals increased fixation times over parts of the text that are most relevant
for answering the question. Motivated by this finding, we propose making
automated reading comprehension more human-like by mimicking human
information-seeking reading behavior during reading comprehension. We
demonstrate that this approach leads to performance gains on multiple choice
question answering in English for a state-of-the-art reading comprehension
model.
