# WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset

arXiv: https://arxiv.org/abs/2303.17876 · HTML: https://mlsanity.com/paper/2303.17876/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 72 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking](https://mlsanity.com/paper/1504.06755/) (2015)
- [Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing](https://mlsanity.com/paper/2102.12060/) (2021)
- [ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation](https://mlsanity.com/paper/1912.00903/) (2019)
- [Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension](https://mlsanity.com/paper/2010.06396/) (2020)
- [CogniVal: A Framework for Cognitive Word Embedding Evaluation](https://mlsanity.com/paper/1909.09001/) (2019)
- [Entity Recognition at First Sight: Improving NER with Eye Movement Information](https://mlsanity.com/paper/1902.10068/) (2019)
- [Relative Importance in Sentence Processing](https://mlsanity.com/paper/2106.03471/) (2021)
- [Bridging Information-Seeking Human Gaze and Machine Reading Comprehension](https://mlsanity.com/paper/2009.14780/) (2020)
- [Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze?](https://mlsanity.com/paper/2205.10226/) (2022)
- [Leveraging Cognitive Features for Sentiment Analysis](https://mlsanity.com/paper/1701.05581/) (2017)
- [Advancing NLP with Cognitive Language Processing Signals](https://mlsanity.com/paper/1904.02682/) (2019)
- [A Survey on Using Gaze Behaviour for Natural Language Processing](https://mlsanity.com/paper/2112.15471/) (2021)
- [The Copenhagen Corpus of Eye Tracking Recordings from Natural Reading of Danish Texts](https://mlsanity.com/paper/2204.13311/) (2022)
- [Every word counts: A multilingual analysis of individual human alignment with model attention](https://mlsanity.com/paper/2210.04963/) (2022)
- [Re-Examining Human Annotations for Interpretable NLP](https://mlsanity.com/paper/2204.04580/) (2022)
- [GazBy: Gaze-Based BERT Model to Incorporate Human Attention in Neural Information Retrieval](https://mlsanity.com/paper/2207.01674/) (2022)

## Built on by (most cited first, 6 of 6)

- [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)
- [OASST-ETC Dataset: Alignment Signals from Eye-tracking Analysis of LLM Responses](https://mlsanity.com/paper/2503.10927/) (2025)
- [Integrating Cognitive Processing Signals into Language Models: A Review of Advances, Applications and Future Directions](https://mlsanity.com/paper/2504.06843/) (2025)
- [Evaluating Webcam-based Gaze Data as an Alternative for Human Rationale Annotations](https://mlsanity.com/paper/2402.19133/) (2024)
- [Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support](https://mlsanity.com/paper/2602.19598/) (2026)

## Abstract (arXiv)

We present WebQAmGaze, a multilingual low-cost eye-tracking-while-reading dataset, designed as the first webcam-based eye-tracking corpus of reading to support the development of explainable computational language processing models. WebQAmGaze includes webcam eye-tracking data from 600 participants of a wide age range naturally reading English, German, Spanish, and Turkish texts. Each participant performs two reading tasks composed of five texts each, a normal reading and an information-seeking task, followed by a comprehension question. We compare the collected webcam data to high-quality eye-tracking recordings. The results show a moderate to strong correlation between the eye movement measures obtained with the webcam compared to those obtained with a commercial eye-tracking device. When validating the data, we find that higher fixation duration on relevant text spans accurately indicates correctness when answering the corresponding questions. This dataset advances webcam-based reading studies and opens avenues to low-cost and diverse data collection. WebQAmGaze is beneficial to learn about the cognitive processes behind question-answering and to apply these insights to computational models of language understanding.