@InProceedings{ostling-grigonyte:2017:BEA,
  author    = {\"{O}stling, Robert  and  Grigonyte, Gintare},
  title     = {Transparent text quality assessment with convolutional neural networks},
  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     = {282--286},
  abstract  = {We present a very simple model for text quality assessment based on a deep
	convolutional neural network, where the only supervision required is one corpus
	of user-generated text of varying quality, and one contrasting text corpus of
	consistently high quality. Our model is able to provide local quality
	assessments in different parts of a text, which allows visual feedback about
	where potentially problematic parts of the text are located, as well as a way
	to evaluate which textual features are captured by our model. We evaluate our
	method on two corpora: a large corpus of manually graded student essays and a
	longitudinal corpus of language learner written production, and find that the
	text quality metric learned by our model is a fairly strong predictor of both
	essay grade and learner proficiency level.},
  url       = {http://www.aclweb.org/anthology/W17-5031}
}