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

TL;DR

This paper introduces a unified attentional network that maps natural reading cognitive signals to various linguistic features, enabling efficient analysis of language processing without controlled experiments.

Contribution

It presents a novel data-driven framework that links cognitive signals to linguistic features across lexical, syntactic, and semantic levels using natural reading data.

Findings

Correlation between eye-tracking features and sentence tense.

Framework effectively detects multiple linguistic features from cognitive signals.

Resonates with neuroscience findings.

Abstract

Cognitive processing signals can be used to improve natural language processing (NLP) tasks. However, it is not clear how these signals correlate with linguistic information. Bridging between human language processing and linguistic features has been widely studied in neurolinguistics, usually via single-variable controlled experiments with highly-controlled stimuli. Such methods not only compromises the authenticity of natural reading, but also are time-consuming and expensive. In this paper, we propose a data-driven method to investigate the relationship between cognitive processing signals and linguistic features. Specifically, we present a unified attentional framework that is composed of embedding, attention, encoding and predicting layers to selectively map cognitive processing signals to linguistic features. We define the mapping procedure as a bridging task and develop 12 bridging tasks for lexical, syntactic and semantic features. The proposed framework only requires cognitive processing signals recorded under natural reading as inputs, and can be used to detect a wide range of linguistic features with a single cognitive dataset. Observations from experiment results resonate with previous neuroscience findings. In addition to this, our experiments also reveal a number of interesting findings, such as the correlation between contextual eye-tracking features and tense of sentence.