  We explore whether it is possible to leverage eye-tracking data in an RNN
dependency parser (for English) when such information is only available during
training, i.e., no aggregated or token-level gaze features are used at
inference time. To do so, we train a multitask learning model that parses
sentences as sequence labeling and leverages gaze features as auxiliary tasks.
Our method also learns to train from disjoint datasets, i.e. it can be used to
test whether already collected gaze features are useful to improve the
performance on new non-gazed annotated treebanks. Accuracy gains are modest but
positive, showing the feasibility of the approach. It can serve as a first step
towards architectures that can better leverage eye-tracking data or other
complementary information available only for training sentences, possibly
leading to improvements in syntactic parsing.
