Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?

Autor:	Servan, Christophe, Berard, Alexandre, Elloumi, Zied, Blanchon, Hervé, Besacier, Laurent
Rok vydání:	2016
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: accepted to COLING 2016 conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1610.01291 Zobrazit plný text záznamu View this record from Arxiv