@InProceedings{adams-roy-krishnapuram:2016:NLPTEA2016,
  author    = {Adams, Oliver  and  Roy, Shourya  and  Krishnapuram, Raghuram},
  title     = {Distributed Vector Representations for Unsupervised Automatic Short Answer Grading},
  booktitle = {Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {20--29},
  abstract  = {We address the problem of automatic short answer grading, evaluating a
	collection of approaches
	inspired by recent advances in distributional text representations. In
	addition, we propose an unsupervised
	approach for determining text similarity using one-to-many alignment of word
	vectors.
	We evaluate the proposed technique across two datasets from different domains,
	namely,
	computer science and English reading comprehension, that additionally vary
	between highschool
	level and undergraduate students. Experiments demonstrate that the proposed
	technique
	often outperforms other compositional distributional semantics approaches as
	well as vector
	space methods such as latent semantic analysis. When combined with a scoring
	scheme, the
	proposed technique provides a powerful tool for tackling the complex problem of
	short answer
	grading. We also discuss a number of other key points worthy of consideration
	in preparing
	viable, easy-to-deploy automatic short-answer grading systems for the
	real-world.},
  url       = {http://aclweb.org/anthology/W16-4904}
}