Guiding automatic MT evaluation by means of linguistic features.

Machine translation (MT) has become increasingly important and popular in the past decade, leading to the development of MT evaluation metrics aiming at automatically assessing MT output. Most of these metrics use reference translations to compare systems output, therefore, they should not only dete...

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Publicado en:Digital Scholarship in the Humanities Vol. 32; no. 4; pp. 761 - 779
Autores principales: Comelles, Elisabet, Arranz, Victoria, Castellón, Irene
Formato: Artículo
Publicado: Oxford University Press / USA Dec2017
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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          Comelles, Elisabet
          Arranz, Victoria
          Castellón, Irene
        affil:
          Universitat de Barcelona, Spain
          ELDA/ELRA, France
      su:
        Machine translating
        Translations
        Language & languages
        Linguistics
        English language
      sug:
        subj:
          Machine translating
          Translations
          Language & languages
          Linguistics
          English language
      ab: Machine translation (MT) has become increasingly important and popular in the past decade, leading to the development of MT evaluation metrics aiming at automatically assessing MT output. Most of these metrics use reference translations to compare systems output, therefore, they should not only detect MT errors but also be able to identify correct equivalent expressions so as not to penalize them when those are not displayed in the reference translations. With the aim of improving MT evaluation metrics a study has been carried out of a wide panorama of linguistic features and their implications. For that purpose a Spanish and an English corpora containing hypothesis and reference translations have been analysed from a linguistic point of view, so that common errors can be detected and positive equivalencies highlighted. This article focuses on this qualitative analysis describing the linguistic phenomena that should be considered when developing an automatic MT evaluation metric. The results of this analysis have been used to develop an automatic MT evaluation metric that takes into account different dimensions of language. A brief review of the metric and its evaluation are also provided.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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