We create WebQAmGaze, a multilingual low-cost eye-tracking-while-reading
dataset, designed to support the development of fair and transparent NLP
models. WebQAmGaze includes webcam eye-tracking data from 332 participants
naturally reading English, Spanish, and German texts. Each participant performs
two reading tasks composed of five texts, a normal reading and an
information-seeking task. After preprocessing the data, we find that fixations
on relevant spans seem to indicate correctness when answering the comprehension
questions. Additionally, we perform a comparative analysis of the data
collected to high-quality eye-tracking data. The results show a moderate
correlation between the features obtained with the webcam-ET compared to those
of a commercial ET device. We believe this data can advance webcam-based
reading studies and open a way to cheaper and more accessible data collection.
WebQAmGaze is useful to learn about the cognitive processes behind question
answering (QA) and to apply these insights to computational models of language
understanding.

创建了 WebQAmGaze—— 一个多语言低成本的阅读时眼动追踪数据集，旨在支持公平和透明的 NLP 模型的开发。WebQAmGaze 包括来自 332 个参与者的网络摄像头眼动跟踪数据，这些参与者以自然的方式阅读英语、西班牙语和德语文本。数据预处理后，我们发现对相关段落的注视似乎提示了对问答理解正确性的影响。此外，我们进行了数据采集的比较分析。结果显示，Webcam-ET 获取的特征与商用 ET 设备的特征之间具有中等相关性。我们认为，这些数据可以推进基于网络摄像头的阅读研究，并开辟了更便宜、更易用的数据采集途径，对学习问答背后认知的过程以及将这些见解应用到语言理解的计算模型中都有帮助。