Gaze-Driven Sentence Simplification for Language Learners: Enhancing Comprehension and Readability

TL;DR

This paper introduces a gaze-driven sentence simplification system that uses eye tracking and machine learning to improve language learners' reading comprehension by providing real-time simplified text, leveraging GPT-3.5.

Contribution

It presents a novel personalized gaze-based system that assesses comprehension and offers automatic sentence simplification to aid language learners.

Findings

System accurately estimates sentence comprehension.

GPT-3.5 simplifies text, improving readability.

Enhanced comprehension demonstrated in learner study.

Abstract

Language learners should regularly engage in reading challenging materials as part of their study routine. Nevertheless, constantly referring to dictionaries is time-consuming and distracting. This paper presents a novel gaze-driven sentence simplification system designed to enhance reading comprehension while maintaining their focus on the content. Our system incorporates machine learning models tailored to individual learners, combining eye gaze features and linguistic features to assess sentence comprehension. When the system identifies comprehension difficulties, it provides simplified versions by replacing complex vocabulary and grammar with simpler alternatives via GPT-3.5. We conducted an experiment with 19 English learners, collecting data on their eye movements while reading English text. The results demonstrated that our system is capable of accurately estimating sentence-level comprehension. Additionally, we found that GPT-3.5 simplification improved readability in terms of traditional readability metrics and individual word difficulty, paraphrasing across different linguistic levels.