Proposition Entailment in Educational Applications using Deep Neural Networks

The next generation of educational applications need to significantly improve the way feedback is offered to both teachers and students. Simply determining coarse-grained entailment relations between the teacher's reference answer as a whole and a student response will not be sufficient. A finer-grained analysis is needed to determine which aspects of the reference answer have been understood and which have not. To this end, we propose an approach that splits the reference answer into its constituent propositions and two methods for detecting entailment relations between each reference answer proposition and a student response. Both methods, one using hand-crafted features and an SVM and the other using word embeddings and deep neural networks, achieve significant improvements over a state-of-the-art system and two alternative approaches.