@InProceedings{tang-EtAl:2016:COLING2,
  author    = {Tang, Haiqing  and  Xiong, Deyi  and  Lopez de Lacalle, Oier  and  Agirre, Eneko},
  title     = {Improving Translation Selection with Supersenses},
  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     = {3114--3123},
  abstract  = {Selecting appropriate translations for source words with multiple meanings
	still remains a challenge for statistical machine translation (SMT). One reason
	for this is that most SMT systems are not good at detecting the proper sense
	for a polysemic word when it appears in different contexts. In this paper, we
	adopt a supersense tagging method to annotate source words with coarse-grained
	ontological concepts. In order to enable the system to choose an appropriate
	translation for a word or phrase according to the annotated supersense of the
	word or phrase, we propose two translation models with supersense knowledge: a
	maximum entropy based model and a supersense embedding model. The effectiveness
	of our proposed models is validated on a large-scale English-to-Spanish
	translation task. Results indicate that our method can significantly improve
	translation quality via correctly conveying the meaning of the source language
	to the target language.
	Author{3}{Affiliation}},
  url       = {http://aclweb.org/anthology/C16-1293}
}