Unknown Word Detection for English as a Second Language (ESL) Learners Using Gaze and Pre-trained Language Models

TL;DR

This paper introduces EyeLingo, a transformer-based system that detects unknown words in real time for ESL learners using gaze data, significantly aiding vocabulary learning and comprehension.

Contribution

The paper presents a novel real-time unknown word detection method combining gaze tracking and language models, with high accuracy and practical application in reading assistance.

Findings

Achieved 97.6% accuracy in unknown word detection

F1-score of 71.1% demonstrating effective prediction

User study shows increased willingness to use and perceived usefulness

Abstract

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.