用眼动+大模型实时识别英语学习者遇到的生词，精准率高达97.6%。
Unknown Word Detection for English as a Second Language (ESL) Learners Using Gaze and Pre-trained Language Models
- 结合文本内容与眼动轨迹，用Transformer模型实时预测生词
- 用户研究显示准确率达97.6%，F1值为71.1%
- 适合需要即时词汇帮助的英语学习者和阅读辅助系统开发者
英语作为第二语言（ESL）的学习者常遇生词，影响理解。自动检测阅读中的未知词可提供即时释义、同义词或上下文解释，帮助学习者自然高效地积累词汇。本文提出EyeLingo，一种基于Transformer的机器学习方法，通过分析文本内容与眼动轨迹，在实时场景下高精度预测未知词。20名参与者的用户研究表明，该方法准确率达97.6%，F1得分为71.1%。我们实现了实时阅读辅助原型系统，用户反馈显示其使用意愿和实用性优于基线方法。
原文摘要 · Abstract (English)
English as a Second Language (ESL) learners often encounter unknown words that hinder their text comprehension. Automatically detecting these words as users read can enable computing systems to provide just-in-time definitions, synonyms, or contextual explanations, thereby helping users learn vocabulary in a natural and seamless manner. This paper presents EyeLingo, a transformer-based machine learning method that predicts the probability of unknown words based on text content and eye gaze trajectory in real time with high accuracy. A 20-participant user study revealed that our method can achieve an accuracy of 97.6%, and an F1-score of 71.1%. We implemented a real-time reading assistance prototype to show the effectiveness of EyeLingo. The user study shows improvement in willingness to use and usefulness compared to baseline methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。