KNUIR at the NTCIR-16 RCIR: Predicting Comprehension Level using Regression Models based on Eye-Tracking Metadata

We participated in the CET sub-task of the NTCIR-16 RCIR. In order to participate in the NTCIR-16 reading comprehension information retrieval (RCIR) CET sub-task, we adopted five regression models: Linear Regression, Random Forest Regressor, Gradient Boosting Regressor, eXtreme Gradient Boosting (XGB) Regressor, and Voting Regressor. We submitted the prediction results of test data to NTCIR- 16 and analyzed the obtained results. Throughout the analysis, we found that Gradient Boosting and Random Forest Regressor generally show better performance with Spearman’s rho of 0.53 and 0.57, respectively. In addition, the feature importance analysis indicated that each participant shows different eye-tracking tendencies for their reading comprehension. Findings in our work may bring insight into the understanding of human reading and information seeking processes with the help of eye-tracking systems by applying various regression models.