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. This metric 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-semantic resources 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.
@article{arxiv.1610.01291,
title = {Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?},
author = {Christophe Servan and Alexandre Berard and Zied Elloumi and Hervé Blanchon and Laurent Besacier},
journal= {arXiv preprint arXiv:1610.01291},
year = {2016}
}