用眼动数据精准预测阅读理解答题结果。
Fine-Grained Prediction of Reading Comprehension from Eye Movements
- 构建三类多模态模型，融合眼动与文本信息。
- 在新文本、新人上仍保持一定预测能力。
- 适合认知研究、教育评估等场景使用。
人类阅读理解能否通过眼动数据评估？本研究利用大规模眼动追踪数据，针对单题阅读理解这一细粒度任务，探索从眼动信号预测阅读理解表现的可行性。研究聚焦于普通阅读与信息检索两种阅读模式，在新文本、新参与者及两者组合条件下评估模型泛化能力，采用三类新型多模态语言模型及文献中已有模型进行对比。结果表明，尽管任务极具挑战性，眼动数据仍包含可用于细粒度阅读理解预测的有效信号。代码与数据将公开共享。
原文摘要 · Abstract (English)
Can human reading comprehension be assessed from eye movements in reading? In this work, we address this longstanding question using large-scale eyetracking data over textual materials that are geared towards behavioral analyses of reading comprehension. We focus on a fine-grained and largely unaddressed task of predicting reading comprehension from eye movements at the level of a single question over a passage. We tackle this task using three new multimodal language models, as well as a battery of prior models from the literature. We evaluate the models' ability to generalize to new textual items, new participants, and the combination of both, in two different reading regimes, ordinary reading and information seeking. The evaluations suggest that although the task is highly challenging, eye movements contain useful signals for fine-grained prediction of reading comprehension. Code and data will be made publicly available.
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