Using neural tensor networks for open ended short answer assessment


In this paper, we present a novel approach to leverage the power of Neural Tensor Networks (NTN) for student answer assessment in intelligent tutoring systems. The approach was evaluated on data collected using a dialogue based intelligent tutoring system (ITS). Particularly, we have experimented with different assessment models that were trained using features generated from knowledge graph embeddings derived with NTN. Our experiments showed that the model trained with the feature vectors generated with NTN, when trained with a combination of domain specific and domain general triplets, performs better than a previously proposed LSTM based approach.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)