@InProceedings{bryant-felice-briscoe:2017:Long,
  author    = {Bryant, Christopher  and  Felice, Mariano  and  Briscoe, Ted},
  title     = {Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {793--805},
  abstract  = {Until now, error type performance for Grammatical Error Correction (GEC)
	systems could only be measured in terms of recall because system output is not
	annotated. To overcome this problem, we introduce ERRANT, a grammatical ERRor
	ANnotation Toolkit designed to automatically extract edits from parallel
	original and corrected sentences and classify them according to a new,
	dataset-agnostic, rule-based framework. This not only facilitates error type
	evaluation at different levels of granularity, but can also be used to reduce
	annotator workload and standardise existing GEC datasets. Human experts rated
	the automatic edits as ``Good'' or ``Acceptable'' in at least 95\% of cases, so
	we applied ERRANT to the system output of the CoNLL-2014 shared task to carry
	out a detailed error type analysis for the first time.},
  url       = {http://aclweb.org/anthology/P17-1074}
}