@InProceedings{bollmann-sogaard:2016:COLING,
  author    = {Bollmann, Marcel  and  S{\o}gaard, Anders},
  title     = {Improving historical spelling normalization with bi-directional LSTMs and multi-task learning},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {131--139},
  abstract  = {Natural-language processing of historical documents is complicated by the
	abundance of variant spellings and lack of annotated data. A common approach is
	to normalize the spelling of historical words to modern forms. We explore the
	suitability of a deep neural network architecture for this task, particularly a
	deep bi-LSTM network applied on a character level. Our model compares well to
	previously established normalization algorithms when evaluated on a diverse set
	of texts from Early New High German. We show that multi-task learning with
	additional normalization data can improve our model's performance further.},
  url       = {http://aclweb.org/anthology/C16-1013}
}