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Keep Eyes on the Sentence: An Interactive Sentence Simplification System for English Learners Based on Eye Tracking and Large Language Models

Published: 11 May 2024 Publication History

Abstract

Language learners should read challenging texts regularly. However, using dictionaries or search engines to look up difficult expressions can be time-consuming and distracting. To address this, we have developed a system combining eye tracking with Large Language Models (LLMs) to simplify sentences automatically, allowing learners to focus on the content. The system incorporates user-tailored models that estimate users’ comprehension of sentences using gaze data and sentence information. The system also features user-triggered simplification, resulting from iterative design improvements. We conducted a user study with 17 English learners where they read English text using either our system or a baseline involving online dictionaries and search engines. Our system significantly improved both reading speed and comprehension, especially for complex sentences. The gaze-based simplification improved concentration on the content, allowing for an interruption-free reading experience. It could assist in daily reading practice, particularly for extensive reading focused on large volumes of text at a rapid pace.

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References

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      cover image ACM Conferences
      CHI EA '24: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems
      May 2024
      4761 pages
      ISBN:9798400703317
      DOI:10.1145/3613905
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Published: 11 May 2024

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      Author Tags

      1. Eye tracking
      2. human-computer interaction
      3. machine learning
      4. sentence simplification

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      Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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      ACM CHI Conference on Human Factors in Computing Systems
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