  Cognitively inspired NLP leverages human-derived data to teach machines about
language processing mechanisms. Recently, neural networks have been augmented
with behavioral data to solve a range of NLP tasks spanning syntax and
semantics. We are the first to exploit neuroscientific data, namely
electroencephalography (EEG), to inform a neural attention model about language
processing of the human brain. The challenge in working with EEG data is that
features are exceptionally rich and need extensive pre-processing to isolate
signals specific to text processing. We devise a method for finding such EEG
features to supervise machine attention through combining theoretically
motivated cropping with random forest tree splits. After this dimensionality
reduction, the pre-processed EEG features are capable of distinguishing two
reading tasks retrieved from a publicly available EEG corpus. We apply these
features to regularise attention on relation classification and show that EEG
is more informative than strong baselines. This improvement depends on both the
cognitive load of the task and the EEG frequency domain. Hence, informing
neural attention models with EEG signals is beneficial but requires further
investigation to understand which dimensions are the most useful across NLP
tasks.
