@InProceedings{horbach-EtAl:2017:BEA,
  author    = {Horbach, Andrea  and  Scholten-Akoun, Dirk  and  Ding, Yuning  and  Zesch, Torsten},
  title     = {Fine-grained essay scoring of a complex writing task for native speakers},
  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     = {357--366},
  abstract  = {Automatic essay scoring is nowadays successfully used even in high-stakes
	tests, but this is mainly limited to holistic scoring of learner essays.
	We present a new dataset of essays written by highly proficient German native
	speakers that is scored using a fine-grained rubric with the goal to provide
	detailed feedback.
	Our experiments with two state-of-the-art scoring systems (a neural and a
	SVM-based one)                                show a large drop in performance
	compared to
	existing
	datasets.
	This demonstrates the need for such datasets that allow to guide research on
	more elaborate essay scoring methods.},
  url       = {http://www.aclweb.org/anthology/W17-5040}
}