A Survey on Using Gaze Behaviour for Natural Language Processing

TL;DR

This survey reviews how gaze behaviour data, collected during cognitive tasks, can be used in NLP applications without requiring real-time eye tracking, focusing on existing corpora and educational applications.

Contribution

It summarizes current research on using gaze behaviour in NLP without recording it at test time and discusses available eye tracking corpora and educational applications.

Findings

Gaze data can improve NLP tasks like essay grading and word identification.

Existing eye tracking corpora are available in multiple languages.

Using gaze behaviour reduces the need for costly real-time data collection.

Abstract

Gaze behaviour has been used as a way to gather cognitive information for a number of years. In this paper, we discuss the use of gaze behaviour in solving different tasks in natural language processing (NLP) without having to record it at test time. This is because the collection of gaze behaviour is a costly task, both in terms of time and money. Hence, in this paper, we focus on research done to alleviate the need for recording gaze behaviour at run time. We also mention different eye tracking corpora in multiple languages, which are currently available and can be used in natural language processing. We conclude our paper by discussing applications in a domain - education - and how learning gaze behaviour can help in solving the tasks of complex word identification and automatic essay grading.