Reading Type Classification based on Generative Models and Bidirectional Long Short-Term Memory

Measuring the attention of users is necessary to design smart Human Computer Interaction (HCI) systems. Particularly, in reading, the reading types, so-called reading, skimming, and scanning are signs to express the degree of attentiveness. Eye movements are informative spatiotemporal data to measure quality of reading. Eye tracking technology is the tool to record eye movements. Even though there is increasing usage of eye trackers in research and especially in psycholinguistics, collecting appropriate task-specific eye movements data is expensitive and time consuming. Moreover, machine learning tools like Recurrent Neural Networks need large enough samples to be trained. Hence, designing a generative model in order to have reliable research-oriented synthetic eye movements is desirable. This paper has two main contributions. First, a generative model in order to synthesize reading, skimming, and scanning in reading is developed. Second, in order to evaluate the generative model, a bidirectional Long ShortTerm Memory (BLSTM) is proposed. It was trained with synthetic data and tested with real-world eye movements to classify reading, skimming, and scanning where more than 95% classification accuracy is achieved. ACM Classification