Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources? - Archive ouverte HAL Access content directly
Conference Papers Year : 2016

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

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. 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.
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Dates and versions

hal-01376948 , version 1 (07-10-2016)

Licence

CC0 - Public Domain Dedication

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Christophe Servan, Alexandre Bérard, Zied Elloumi, Hervé Blanchon, Laurent Besacier. Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?. COLING 2016, ANLP & ICCL, Dec 2016, Osaka, Japan. ⟨hal-01376948⟩
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