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Cognitive processing signals can be used to improve natural languageprocessing (NLP) tasks. However, it is not clear how these signals correlatewith linguistic information. Bridging between human language processing andlinguistic features has been widely studied in neurolinguistics, usually viasingle-variable controlled experiments with highly-controlled stimuli. Suchmethods not only compromises the authenticity of natural reading, but also aretime-consuming and expensive. In this paper, we propose a data-driven method toinvestigate the relationship between cognitive processing signals andlinguistic features. Specifically, we present a unified attentional frameworkthat is composed of embedding, attention, encoding and predicting layers toselectively map cognitive processing signals to linguistic features. We definethe mapping procedure as a bridging task and develop 12 bridging tasks forlexical, syntactic and semantic features. The proposed framework only requirescognitive processing signals recorded under natural reading as inputs, and canbe used to detect a wide range of linguistic features with a single cognitivedataset. Observations from experiment results resonate with previousneuroscience findings. In addition to this, our experiments also reveal anumber of interesting findings, such as the correlation between contextualeye-tracking features and tense of sentence.

Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network