# Bridging Information-Seeking Human Gaze and Machine Reading Comprehension

arXiv: https://arxiv.org/abs/2009.14780 · HTML: https://mlsanity.com/paper/2009.14780/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 20 of 20)

- [Multilingual Language Models Predict Human Reading Behavior](https://mlsanity.com/paper/2104.05433/) (2021)
- [Relative Importance in Sentence Processing](https://mlsanity.com/paper/2106.03471/) (2021)
- [Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze?](https://mlsanity.com/paper/2205.10226/) (2022)
- [Fine-Grained Prediction of Reading Comprehension from Eye Movements](https://mlsanity.com/paper/2410.04484/) (2024)
- [WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset](https://mlsanity.com/paper/2303.17876/) (2023)
- [Decoding Reading Goals from Eye Movements](https://mlsanity.com/paper/2410.20779/) (2024)
- [PoTeC: A German Naturalistic Eye-tracking-while-reading Corpus](https://mlsanity.com/paper/2403.00506/) (2024)
- [EMTeC: A Corpus of Eye Movements on Machine-Generated Texts](https://mlsanity.com/paper/2408.04289/) (2024)
- [Human Attention during Goal-directed Reading Comprehension Relies on Task Optimization](https://mlsanity.com/paper/2107.05799/) (2021)
- [Comparing Humans and Models on a Similar Scale: Towards Cognitive Gender Bias Evaluation in Coreference Resolution](https://mlsanity.com/paper/2305.15389/) (2023)
- [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)
- [Does Attention Mechanism Possess the Feature of Human Reading? A Perspective of Sentiment Classification Task](https://mlsanity.com/paper/2209.03557/) (2022)
- [Language Cognition and Language Computation -- Human and Machine Language Understanding](https://mlsanity.com/paper/2301.04788/) (2023)
- [Perturbation-based Self-supervised Attention for Attention Bias in Text Classification](https://mlsanity.com/paper/2305.15684/) (2023)
- [Deep Understanding based Multi-Document Machine Reading Comprehension](https://mlsanity.com/paper/2204.03494/) (2022)
- [An Understanding-Oriented Robust Machine Reading Comprehension Model](https://mlsanity.com/paper/2207.00187/) (2022)
- [Cross-corpus Readability Compatibility Assessment for English Texts](https://mlsanity.com/paper/2306.09704/) (2023)
- [Decoding Open-Ended Information Seeking Goals from Eye Movements in Reading](https://mlsanity.com/paper/2505.02872/) (2025)
- [Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support](https://mlsanity.com/paper/2602.19598/) (2026)

## Abstract (arXiv)

In this work, we analyze how human gaze during reading comprehension is conditioned on the given reading comprehension question, and whether this signal can be beneficial for machine reading comprehension. To this end, we collect a new eye-tracking dataset with a large number of participants engaging in a multiple choice reading comprehension task. Our analysis of this data reveals increased fixation times over parts of the text that are most relevant for answering the question. Motivated by this finding, we propose making automated reading comprehension more human-like by mimicking human information-seeking reading behavior during reading comprehension. We demonstrate that this approach leads to performance gains on multiple choice question answering in English for a state-of-the-art reading comprehension model.