@InProceedings{servan-EtAl:2016:COLING,
  author    = {Servan, Christophe  and  Berard, Alexandre  and  elloumi, zied  and  Blanchon, Herv\'{e}  and  Besacier, Laurent},
  title     = {Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?},
  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     = {1159--1168},
  abstract  = {This paper presents an approach combining lexico-semantic resources and
	distributed representations of words applied to the evaluation in machine
	translation (MT). This study is made through the enrichment of a well-known MT
	evaluation metric: METEOR. METEOR enables an approximate match (synonymy or
	morphological similarity) between an automatic and a reference translation. Our
	experiments are made in the framework of the Metrics task of WMT 2014. We show
	that distributed representations are a good alternative to
	lexico-semanticresources for MT evaluation and they can even bring interesting
	additional information. The augmented versions of METEOR, using vector
	representations, are made available on our Github page.},
  url       = {http://aclweb.org/anthology/C16-1110}
}