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The contribution of linguistic features to automatic machine translation evaluation

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dc.contributor.authorGiménez, Jesús
dc.contributor.authorVerdejo, Felisa
dc.contributor.authorAmigo Cabrera, Enrique
dc.contributor.authorGonzalo Arroyo, Julio Antonio
dc.date.accessioned2024-05-21T13:03:38Z
dc.date.available2024-05-21T13:03:38Z
dc.date.issued2009-08-02
dc.description.abstractA number of approaches to Automatic MT Evaluation based on deep linguistic knowledge have been suggested. However, n-gram based metrics are still today the dominant approach. The main reason is that the advantages of employing deeper linguistic information have not been clarified yet. In this work, we propose a novel approach for meta-evaluation of MT evaluation metrics, since correlation cofficient against human judges do not reveal details about the advantages and disadvantages of particular metrics. We then use this approach to investigate the benefits of introducing linguistic features into evaluation metrics. Overall, our experiments show that (i) both lexical and linguistic metrics present complementary advantages and (ii) combining both kinds of metrics yields the most robust metaevaluation performance.es
dc.description.versionversión publicada
dc.identifier.urihttps://hdl.handle.net/20.500.14468/19994
dc.language.isoen
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.titleThe contribution of linguistic features to automatic machine translation evaluationes
dc.typeconference proceedingsen
dc.typeactas de congresoes
dspace.entity.typePublication
relation.isAuthorOfPublicationf96c6e59-3a7a-4b0c-9b10-deec22f8c06b
relation.isAuthorOfPublication0e0d6c85-2d8e-4fb3-9640-8ad17e875fcc
relation.isAuthorOfPublication.latestForDiscoveryf96c6e59-3a7a-4b0c-9b10-deec22f8c06b
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