论文标题
部分可观测时空混沌系统的无模型预测
Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns
论文作者
论文摘要
NLP和机器学习方法的联合使用越来越感兴趣，以预测自然主义阅读期间的凝视模式。尽管通过使用基于变压器的语言模型获得了有希望的结果，但很少有工作将这种模型的性能与一般文本特征联系起来。在本文中，我们报告了两个自然主义阅读和两个语言模型（Bert和GPT-2）的实验。在所有实验中，我们测试了一系列特征的效果，以预测人类阅读行为分为五类（句法复杂性，词汇丰富度，基于寄存器的多单词组合，可读性和心理语言词属性）。我们的实验表明，基于变压器的语言模型的功能和架构在预测自然主义阅读过程中的多种眼睛追踪措施中起着作用。我们还报告了旨在使用SP-LIME确定不同组特征的相对重要性的实验结果。
There is a growing interest in the combined use of NLP and machine learning methods to predict gaze patterns during naturalistic reading. While promising results have been obtained through the use of transformer-based language models, little work has been undertaken to relate the performance of such models to general text characteristics. In this paper we report on experiments with two eye-tracking corpora of naturalistic reading and two language models (BERT and GPT-2). In all experiments, we test effects of a broad spectrum of features for predicting human reading behavior that fall into five categories (syntactic complexity, lexical richness, register-based multiword combinations, readability and psycholinguistic word properties). Our experiments show that both the features included and the architecture of the transformer-based language models play a role in predicting multiple eye-tracking measures during naturalistic reading. We also report the results of experiments aimed at determining the relative importance of features from different groups using SP-LIME.
