Most previous studies integrate cognitive language processing signals (e.g.,
eye-tracking or EEG data) into neural models of natural language processing
(NLP) just by directly concatenating word embeddings with cognitive features,
ignoring the gap between the two modalities (i.e., textual vs. cognitive) and
noise in cognitive features. In this paper, we propose a CogAlign approach to
these issues, which learns to align textual neural representations to cognitive
features. In CogAlign, we use a shared encoder equipped with a modality
discriminator to alternatively encode textual and cognitive inputs to capture
their differences and commonalities. Additionally, a text-aware attention
mechanism is proposed to detect task-related information and to avoid using
noise in cognitive features. Experimental results on three NLP tasks, namely
named entity recognition, sentiment analysis and relation extraction, show that
CogAlign achieves significant improvements with multiple cognitive features
over state-of-the-art models on public datasets. Moreover, our model is able to
transfer cognitive information to other datasets that do not have any cognitive
processing signals