Gaze-Driven Sentence Simplification for Language Learners: Enhancing
  Comprehension and Readability
Taichi Higasa Keitaro Tanaka Qi Feng Shigeo Morishima
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.