@InProceedings{rei:2017:BEA,
  author    = {Rei, Marek},
  title     = {Detecting Off-topic Responses to Visual Prompts},
  booktitle = {Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {188--197},
  abstract  = {Automated methods for essay scoring have made great progress in recent years,
	achieving accuracies very close to human annotators. 
	However, a known weakness of such automated scorers is not taking into account
	the semantic relevance of the submitted text.
	While there is existing work on detecting answer relevance given a textual
	prompt, very little previous research has been done to incorporate visual
	writing prompts.
	We propose a neural architecture and several extensions for detecting off-topic
	responses to visual prompts and evaluate it on a dataset of texts written by
	language learners.},
  url       = {http://www.aclweb.org/anthology/W17-5020}
}