用少量眼动数据，通过LSTM+轻量网络预测阅读能力得分
Reading ability detection using eye-tracking data with LSTM-based few-shot learning
- 结合LSTM与轻量神经网络构建回归模型
- 仅用68人数据即达到更高预测准确率
- 适合教育评估与少样本学习研究者
阅读能力检测在现代教育领域至关重要。本文提出一种基于少量受试者（如68人）眼动数据的阅读能力分数预测方法。该方法结合长短期记忆网络（LSTM）与轻量级神经网络，构建回归模型以实现分数预测。实验表明，在少样本学习策略下，该方法在阅读能力检测的分数预测任务中优于以往方法。代码可在 https://github.com/pumpkinLNX/LSTM-eye-tracking-pytorch.git 获取。
原文摘要 · Abstract (English)
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
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。