  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.
