Unknown Word Detection for English as a Second Language (ESL) Learners Using Gaze and Pre-trained Language Models
This work addresses the problem of vocabulary acquisition for English as a Second Language learners, providing a tool to enhance their reading experience and learning outcomes.
The authors tackled the problem of detecting unknown words for English as a Second Language learners, achieving an accuracy of 97.6% and an F1-score of 71.1% with their EyeLingo method. This enables computing systems to provide just-in-time definitions and explanations, improving text comprehension.
English as a Second Language (ESL) learners often encounter unknown words that hinder their text comprehension. Automatically detecting these words as users read can enable computing systems to provide just-in-time definitions, synonyms, or contextual explanations, thereby helping users learn vocabulary in a natural and seamless manner. This paper presents EyeLingo, a transformer-based machine learning method that predicts the probability of unknown words based on text content and eye gaze trajectory in real time with high accuracy. A 20-participant user study revealed that our method can achieve an accuracy of 97.6%, and an F1-score of 71.1%. We implemented a real-time reading assistance prototype to show the effectiveness of EyeLingo. The user study shows improvement in willingness to use and usefulness compared to baseline methods.