Poster: Unknown Word Detection for ESL Learners Using Gaze and Pre-trained Language Models

For English as a Second Language (ESL) learners, unknown words significantly hinder reading fluency. We present EyeLingo, a novel method that detects unknown words in real-time by integrating user gaze trajectories with pre-trained language models (PLMs). Unlike prior gaze-only methods whose performance is highly sensitive to eye-tracking accuracy, EyeLingo leverages the linguistic context captured by RoBERTa to compensate for noisy gaze data. Our system achieves 97.6% accuracy (F1-score 71.1%) with professional eye trackers and maintains 97.3% accuracy (F1-score 65.1%) using commodity webcams.