@InProceedings{jin-kann:2017:SCLeM,
  author    = {Jin, Huiming  and  Kann, Katharina},
  title     = {Exploring Cross-Lingual Transfer of Morphological Knowledge In Sequence-to-Sequence Models},
  booktitle = {Proceedings of the First Workshop on Subword and Character Level Models in NLP},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {70--75},
  abstract  = {Multi-task training is an effective method to mitigate the data sparsity
	problem. 
	It has recently been applied for cross-lingual transfer learning for paradigm
	completion---the task of producing inflected forms of lemmata---with
	sequence-to-sequence networks.
	However, it is still vague how the model transfers knowledge across languages,
	as well as if and which information is shared.
	To investigate this, we propose a set of data-dependent experiments using an
	existing 
	encoder-decoder recurrent neural network for the task. Our results show that 
	indeed the performance gains surpass a pure regularization effect and that
	knowledge about language and 
	morphology can be transferred.},
  url       = {http://www.aclweb.org/anthology/W17-4110}
}