@InProceedings{zhang-EtAl:2016:COLING5,
  author    = {Zhang, Biao  and  Xiong, Deyi  and  su, jinsong  and  Duan, Hong  and  Zhang, Min},
  title     = {Bilingual Autoencoders with Global Descriptors for Modeling Parallel Sentences},
  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     = {2548--2558},
  abstract  = {Parallel sentence representations are important for bilingual and cross-lingual
	tasks in natural language
	processing. In this paper, we explore a bilingual autoencoder approach to model
	parallel
	sentences. We extract sentence-level global descriptors (e.g. min, max) from
	word embeddings,
	and construct two monolingual autoencoders over these descriptors on the source
	and target language.
	In order to tightly connect the two autoencoders with bilingual
	correspondences, we force
	them to share the same decoding parameters and minimize a corpus-level semantic
	distance between
	the two languages. Being optimized towards a joint objective function of
	reconstruction
	and semantic errors, our bilingual antoencoder is able to learn
	continuous-valued latent representations
	for parallel sentences. Experiments on both intrinsic and extrinsic evaluations
	on statistical
	machine translation tasks show that our autoencoder achieves substantial
	improvements over
	the baselines.},
  url       = {http://aclweb.org/anthology/C16-1240}
}