Unknown Word Detection for English as a Second Language (ESL) Learners Using Gaze and Pre-trained Language Models
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910828035309568 |
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| author | Ding, Jiexin Zhao, Bowen Wang, Yuntao Liu, Xinyun Hao, Rui Chatterjee, Ishan Shi, Yuanchun |
| author_facet | Ding, Jiexin Zhao, Bowen Wang, Yuntao Liu, Xinyun Hao, Rui Chatterjee, Ishan Shi, Yuanchun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unknown Word Detection for English as a Second Language (ESL) Learners Using Gaze and Pre-trained Language Models Ding, Jiexin Zhao, Bowen Wang, Yuntao Liu, Xinyun Hao, Rui Chatterjee, Ishan Shi, Yuanchun Human-Computer Interaction Computation and Language 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. |
| title | Unknown Word Detection for English as a Second Language (ESL) Learners Using Gaze and Pre-trained Language Models |
| topic | Human-Computer Interaction Computation and Language |
| url | https://arxiv.org/abs/2502.10378 |