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        <title>Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?</title>
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    <name type="personal">
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    <name type="personal">
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            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?
%A Servan, Christophe
%A Bérard, Alexandre
%A Elloumi, Zied
%A Blanchon, Hervé
%A Besacier, Laurent
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F servan-etal-2016-word2vec-vs
%X 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.
%U https://aclanthology.org/C16-1110
%P 1159-1168

Markdown (Informal)

[Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?](https://aclanthology.org/C16-1110) (Servan et al., COLING 2016)

• Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources? (Servan et al., COLING 2016)

ACL

• Christophe Servan, Alexandre Bérard, Zied Elloumi, Hervé Blanchon, and Laurent Besacier. 2016. Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources?. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1159–1168, Osaka, Japan. The COLING 2016 Organizing Committee.