Proposition Entailment in Educational Applications Using Deep Neural Networks

Authors

  • Florin Bulgarov University of North Texas
  • Rodney Nielsen University of North Texas

Keywords:

educational applications, deep neural networks, machine learning, word embeddings, entailment

Abstract

To have a more meaningful impact, educational applications need to significantly improve the way feedback is offered to teachers and students. We propose two methods for determining propositional-level entailment relations between a reference answer and a student's 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.

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Published

2018-04-29

How to Cite

Bulgarov, F., & Nielsen, R. (2018). Proposition Entailment in Educational Applications Using Deep Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12189