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.

这篇论文介绍了一种新颖的凝视驱动的句子简化系统，旨在提高语言学习者的阅读理解能力。通过结合凝视特征和语言特征来评估句子理解，系统识别到理解困难时，通过 GPT-3.5 将复杂的词汇和语法替换为简化的替代词汇和语法，从而提供简化版本，收集了 19 名英语学习者阅读英文文本时的眼动数据，实验结果表明我们的系统能够准确估计句子级别的理解能力，此外，我们还发现 GPT-3.5 的简化在传统可读性指标和个别单词难度、不同语言层面的释义方面提高了可读性。