Gaze-Driven Sentence Simplification for Language Learners: Enhancing Comprehension and Readability
This addresses the challenge of reading comprehension for language learners by reducing distractions from dictionary use, though it is incremental as it builds on existing gaze and simplification methods.
The paper tackled the problem of language learners struggling with complex texts by developing a gaze-driven sentence simplification system that uses eye gaze and linguistic features to assess comprehension and provide GPT-3.5-based simplifications, resulting in accurate sentence-level comprehension estimation and improved readability metrics in an experiment with 19 learners.
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.