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

arXiv: https://arxiv.org/abs/2310.00355 · HTML: https://mlsanity.com/paper/2310.00355/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 30 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Neural CRF Model for Sentence Alignment in Text Simplification](https://mlsanity.com/paper/2005.02324/) (2020)
- [BERT Embeddings for Automatic Readability Assessment](https://mlsanity.com/paper/2106.07935/) (2021)
- [LFTK: Handcrafted Features in Computational Linguistics](https://mlsanity.com/paper/2305.15878/) (2023)
- [Sentence Simplification via Large Language Models](https://mlsanity.com/paper/2302.11957/) (2023)
- [GazeReader: Detecting Unknown Word Using Webcam for English as a Second Language (ESL) Learners](https://mlsanity.com/paper/2303.10443/) (2023)

## Built on by (most cited first, 1 of 1)

- [Robust Multilingual Text-to-Pictogram Mapping for Scalable Reading Rehabilitation](https://mlsanity.com/paper/2603.24536/) (2026)

## Abstract (arXiv)

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.