# A Survey on Using Gaze Behaviour for Natural Language Processing

arXiv: https://arxiv.org/abs/2112.15471 · HTML: https://mlsanity.com/paper/2112.15471/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 82 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [Automatic Text Scoring Using Neural Networks](https://mlsanity.com/paper/1606.04289/) (2016)
- [A Report on the Complex Word Identification Shared Task 2018](https://mlsanity.com/paper/1804.09132/) (2018)
- [Entity Recognition at First Sight: Improving NER with Eye Movement Information](https://mlsanity.com/paper/1902.10068/) (2019)
- [Improving sentence compression by learning to predict gaze](https://mlsanity.com/paper/1604.03357/) (2016)
- [SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring](https://mlsanity.com/paper/1711.04981/) (2017)
- [Leveraging Cognitive Features for Sentiment Analysis](https://mlsanity.com/paper/1701.05581/) (2017)
- [Co-Attention Based Neural Network for Source-Dependent Essay Scoring](https://mlsanity.com/paper/1908.01993/) (2019)
- [Predicting Native Language from Gaze](https://mlsanity.com/paper/1704.07398/) (2017)
- [Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour](https://mlsanity.com/paper/1810.04839/) (2018)
- [Harnessing Cognitive Features for Sarcasm Detection](https://mlsanity.com/paper/1701.05574/) (2017)

## Built on by (most cited first, 11 of 11)

- [Synthesizing Human Gaze Feedback for Improved NLP Performance](https://mlsanity.com/paper/2302.05721/) (2023)
- [Seeing Eye to AI: Human Alignment via Gaze-Based Response Rewards for Large Language Models](https://mlsanity.com/paper/2410.01532/) (2024)
- [The Copenhagen Corpus of Eye Tracking Recordings from Natural Reading of Danish Texts](https://mlsanity.com/paper/2204.13311/) (2022)
- [WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset](https://mlsanity.com/paper/2303.17876/) (2023)
- [Longer Fixations, More Computation: Gaze-Guided Recurrent Neural Networks](https://mlsanity.com/paper/2311.00159/) (2023)
- [OASST-ETC Dataset: Alignment Signals from Eye-tracking Analysis of LLM Responses](https://mlsanity.com/paper/2503.10927/) (2025)
- [A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models](https://mlsanity.com/paper/2212.09873/) (2022)
- [Integrating Cognitive Processing Signals into Language Models: A Review of Advances, Applications and Future Directions](https://mlsanity.com/paper/2504.06843/) (2025)
- [Describing Images textitFast and Slow: Quantifying and Predicting the Variation in Human Signals during Visuo-Linguistic Processes](https://mlsanity.com/paper/2402.01352/) (2024)
- [Revising with a Backward Glance: Regressions and Skips during Reading as Cognitive Signals for Revision Policies in Incremental Processing](https://mlsanity.com/paper/2310.18229/) (2023)
- [Your Mouse and Eyes Secretly Leak Your Preference: LLM Alignment using Implicit Feedback from Users](https://mlsanity.com/paper/2606.20482/) (2026)

## Abstract (arXiv)

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.