WebQAmGaze：一个多语言网络摄像头阅读眼动追踪数据集
WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset
AI总结：
本文提出WebQAmGaze，首个基于网络摄像头的多语言阅读眼动追踪数据集，含600名参与者数据，验证了低成本采集的可行性，并支持问答认知研究。
摘要：
我们提出了WebQAmGaze，一个多语言低成本阅读眼动追踪数据集，设计为首个基于网络摄像头的阅读眼动语料库，以支持可解释计算语言处理模型的发展。WebQAmGaze包含来自600名年龄范围广泛的参与者的网络摄像头眼动数据，这些参与者自然阅读英语、德语、西班牙语和土耳其语文本。每位参与者执行两项阅读任务，每项任务包含五篇文本，一项为正常阅读，另一项为信息寻求任务，随后回答一个理解性问题。我们将收集的网络摄像头数据与高质量眼动追踪记录进行比较。结果显示，与商用眼动追踪设备相比，通过网络摄像头获得的眼动测量指标之间存在中度至强相关性。在验证数据时，我们发现对相关文本片段更长的注视持续时间能准确指示回答相应问题的正确性。该数据集推进了基于网络摄像头的阅读研究，并为低成本多样化的数据收集开辟了途径。WebQAmGaze有助于了解问答背后的认知过程，并将这些见解应用于语言理解的计算模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
