Ranking Multiple Choice Question Distractors using Semantically Informed Neural Networks

Abstract

Automatically generating or ranking distractors for multiple-choice questions (MCQs) is still a challenging problem. In this work, we have focused towards automatic ranking of distractors for MCQs. Accordingly, we have proposed an semantically aware CNN-BiLSTM model. We evaluate our model with different word level embeddings as input over two different openly available datasets. Experimental results demonstrate our proposed model surpasses the performance of the existing baseline models. Furthermore, we have observed that intelligently incorporating word level semantic information along with context specific word embeddings boost up the predictive performance of distractors, which is a promising direction for further research.