Human brain activity for machine attention

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 with direct cognitive measures. Part of the challenge in working with EEG 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. This method considerably reduces the number of dimensions of the EEG data. We employ the method on a publicly available EEG corpus and demonstrate that the pre-processed EEG features are capable of distinguishing two reading tasks. We apply these features to regularise attention on relation classification and show that EEG is more informative than strong baselines. This improvement, however, is dependent on both the cognitive load of the task and the EEG frequency domain. Hence, informing neural attention models through EEG signals has benefits but requires further investigation to understand which dimensions are most useful across NLP tasks.