ML Anthology

Proposition Entailment in Educational Applications Using Deep Neural Networks

Florin Adrian Bulgarov, Rodney Nielsen
AAAI 2018 pp. 5045-5052
doi:10.1609/AAAI.V32I1.12189 /aaai/2018/bulgarov2018aaai-proposition-a/

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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Cite

Text

Bulgarov and Nielsen. "Proposition Entailment in Educational Applications Using Deep Neural Networks." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12189

Markdown

[Bulgarov and Nielsen. "Proposition Entailment in Educational Applications Using Deep Neural Networks." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/bulgarov2018aaai-proposition-a/) doi:10.1609/AAAI.V32I1.12189

BibTeX

@inproceedings{bulgarov2018aaai-proposition-a,
  title     = {{Proposition Entailment in Educational Applications Using Deep Neural Networks}},
  author    = {Bulgarov, Florin Adrian and Nielsen, Rodney},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {5045-5052},
  doi       = {10.1609/AAAI.V32I1.12189},
  url       = {https://mlanthology.org/aaai/2018/bulgarov2018aaai-proposition-a/}
}