@InProceedings{vylomova-EtAl:2017:EACLshort,
  author    = {Vylomova, Ekaterina  and  Cotterell, Ryan  and  Baldwin, Timothy  and  Cohn, Trevor},
  title     = {Context-Aware Prediction of Derivational Word-forms},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {118--124},
  abstract  = {Derivational morphology is a fundamental and complex characteristic of
	language.
	In this paper we propose a new task of predicting the derivational form
	of a given base-form lemma that is appropriate for a given context.
	We present an encoder-decoder style neural network to produce a
	derived form character-by-character, based on its corresponding
	character-level representation of the base form and the context. 
	We demonstrate that our model is able to generate valid context-sensitive 
	derivations from known base forms, but is less accurate under lexicon agnostic
	setting.},
  url       = {http://www.aclweb.org/anthology/E17-2019}
}