@InProceedings{guzman-EtAl:2016:COLING,
  author    = {Guzm\'{a}n, Francisco  and  Bouamor, Houda  and  Baly, Ramy  and  Habash, Nizar},
  title     = {Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings},
  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     = {1398--1408},
  abstract  = {Evaluation of machine translation (MT) into morphologically rich languages
	(MRL) has not been
	well studied despite posing many challenges. In this paper, we explore the use
	of embeddings obtained from different levels of lexical and morpho-syntactic
	linguistic analysis and show that they improve MT evaluation into an MRL.
	Specifically we report on Arabic, a language with complex and rich morphology.
	Our results show that using a neural-network model with different input
	representations produces results that clearly outperform the state-of-the-art
	for MT evaluation into Arabic, by almost over 75% increase in correlation with
	human judgments on pairwise MT evaluation quality task. More importantly, we
	demonstrate the usefulness of morpho-syntactic representations to model
	sentence similarity for MT evaluation and address complex linguistic phenomena
	of Arabic.},
  url       = {http://aclweb.org/anthology/C16-1132}
}