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.
翻译：自动检测未知单词的技术可以为英语作为第二语言（ESL）学习者提供新的应用，从而改善他们的阅读体验。然而，大多数现代未知单词检测方法需要具有高精度的专用眼动跟踪设备，这些设备不易为终端用户所获取。在本文中，我们提出了GazeReader，一种仅使用网络摄像头的未知单词检测方法。GazeReader跟踪学习者的视线，然后应用基于Transformer的机器学习模型，对文本信息进行编码以定位未知单词。我们应用了术语频率、词性以及命名实体识别等知识增强技术以提高性能。用户研究表明，我们方法的准确率和F1-Score分别为98.09％和75.73％。最后，我们探讨了ESL阅读的设计范围，并讨论了发现。
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