Towards Predicting Difficulty of Reading Comprehension Questions

We present a corpus and approach to deduce the difficulty of questions asked in a reading comprehension test. A feature-driven model is designed that associates each question with a difficulty level. This would eliminate the laborious task of manually annotating questions in a computerized testing environment. Experiments performed on our corpus show that our model can classify questions with a micro F-score of 0.68.