# Reading ability detection using eye-tracking data with LSTM-based few-shot learning

arXiv: https://arxiv.org/abs/2409.08798 · HTML: https://mlsanity.com/paper/2409.08798/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Episodic Training for Domain Generalization](https://mlsanity.com/paper/1902.00113/) (2019)
- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)
- [Meta-Learning with Task-Adaptive Loss Function for Few-Shot Learning](https://mlsanity.com/paper/2110.03909/) (2021)
- [Explainable artificial intelligence model to predict acute critical illness from electronic health records](https://mlsanity.com/paper/1912.01266/) (2019)
- [Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation](https://mlsanity.com/paper/2106.06908/) (2021)
- [Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey](https://mlsanity.com/paper/2112.10006/) (2021)
- [LibFewShot: A Comprehensive Library for Few-shot Learning](https://mlsanity.com/paper/2109.04898/) (2021)
- [Few-Shot Learning with Class Imbalance](https://mlsanity.com/paper/2101.02523/) (2021)
- [What Matters For Meta-Learning Vision Regression Tasks?](https://mlsanity.com/paper/2203.04905/) (2022)
- [Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview](https://mlsanity.com/paper/2211.08352/) (2022)
- [Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence models](https://mlsanity.com/paper/2210.09819/) (2022)
- [EEG-Based Driver Drowsiness Estimation Using Feature Weighted Episodic Training](https://mlsanity.com/paper/1909.11456/) (2019)
- [Are LSTMs Good Few-Shot Learners?](https://mlsanity.com/paper/2310.14139/) (2023)

## Abstract (arXiv)

Reading ability detection is important in modern educational field. In this paper, a method of predicting scores of reading ability is proposed, using the eye-tracking data of a few subjects (e.g., 68 subjects). The proposed method built a regression model for the score prediction by combining Long Short Time Memory (LSTM) and light-weighted neural networks. Experiments show that with few-shot learning strategy, the proposed method achieved higher accuracy than previous methods of score prediction in reading ability detection. The code can later be downloaded at https://github.com/pumpkinLNX/LSTM-eye-tracking-pytorch.git