@InProceedings{siahbani-sarkar:2017:EACLshort,
  author    = {Siahbani, Maryam  and  Sarkar, Anoop},
  title     = {Lexicalized Reordering for Left-to-Right Hierarchical Phrase-based Translation},
  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     = {612--618},
  abstract  = {Phrase-based and hierarchical phrase-based (Hiero) translation models differ
	radically in the way reordering is modeled. Lexicalized reordering models play
	an important role in phrase-based MT and such models have been added to
	CKY-based decoders for Hiero. Watanabe et al. (2006) proposed a promising
	decoding algorithm for Hiero (LR-Hiero) that visits input spans in arbitrary
	order and produces the translation in left to right (LR) order which leads to
	far fewer language model calls and leads to a considerable speedup in decoding.
	We introduce a novel shift-reduce algorithm to LR-Hiero to decode with our
	lexicalized reordering model (LRM) and show that it improves translation
	quality for Czech-English, Chinese-English and German-English.},
  url       = {http://www.aclweb.org/anthology/E17-2097}
}