GazeReader: Detecting Unknown Word Using Webcam for English as a Second Language (ESL) Learners
This addresses the accessibility issue for ESL learners by providing a low-cost alternative to expensive eye-tracking hardware, though it is incremental as it adapts existing techniques to a new input modality.
The paper tackled the problem of detecting unknown words for ESL learners by proposing GazeReader, a method that uses only a webcam instead of dedicated eye-tracking devices, achieving an accuracy of 98.09% and an F1-score of 75.73% in a user study.
Automatic unknown word detection techniques can enable new applications for assisting English as a Second Language (ESL) learners, thus improving their reading experiences. However, most modern unknown word detection methods require dedicated eye-tracking devices with high precision that are not easily accessible to end-users. In this work, we propose GazeReader, an unknown word detection method only using a webcam. GazeReader tracks the learner's gaze and then applies a transformer-based machine learning model that encodes the text information to locate the unknown word. We applied knowledge enhancement including term frequency, part of speech, and named entity recognition to improve the performance. The user study indicates that the accuracy and F1-score of our method were 98.09% and 75.73%, respectively. Lastly, we explored the design scope for ESL reading and discussed the findings.