@InProceedings{zhou-neubig:2017:Long,
  author    = {Zhou, Chunting  and  Neubig, Graham},
  title     = {Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction},
  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     = {310--320},
  abstract  = {Labeled sequence transduction is a task of transforming one sequence into
	another sequence that satisfies desiderata specified by a set of labels. In
	this paper we propose multi-space variational encoder-decoders, a new model for
	labeled sequence transduction with semi-supervised learning. The generative
	model can use neural networks to handle both discrete and continuous latent
	variables to exploit various features of data. Experiments show that our model
	provides not only a powerful supervised framework but also can effectively take
	advantage of the unlabeled data. On the SIGMORPHON morphological inflection
	benchmark, our model outperforms single-model state-of-art results by a large
	margin for the majority of languages.},
  url       = {http://aclweb.org/anthology/P17-1029}
}