@InProceedings{rei-yannakoudakis:2017:BEA,
  author    = {Rei, Marek  and  Yannakoudakis, Helen},
  title     = {Auxiliary Objectives for Neural Error Detection Models},
  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     = {33--43},
  abstract  = {We investigate the utility of different auxiliary objectives and training
	strategies within a neural sequence labeling approach to error detection in
	learner writing. 
	Auxiliary costs provide the model with additional linguistic information,
	allowing it to learn general-purpose compositional features that can then be
	exploited for other objectives.
	Our experiments show that a joint learning approach trained with parallel
	labels on in-domain data improves performance over the previous best error
	detection system. 
	While the resulting model has the same number of parameters, the additional
	objectives allow it to be optimised more efficiently and achieve better
	performance.},
  url       = {http://www.aclweb.org/anthology/W17-5004}
}