November 2, 2019 Conference paper Open Access

Macé, Valentin; Servan, Christophe

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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>In Machine Translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a simple yet promising approach to add contextual information in Neural Machine Translation. We present a method to add source context that capture the whole document with accurate boundaries, taking every word into account. We provide this additional information to a Transformer model and study the impact of our method on three language pairs. The proposed approach obtains promising results in the English-German, English-French and French-English document-level translation tasks. We observe interesting cross-sentential behaviors where the model learns to use document-level information to improve translation coherence.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "QWANT RESEARCH - 7 Rue Spontini, 75116 Paris, France", 
      "@type": "Person", 
      "name": "Mac\u00e9, Valentin"
    }, 
    {
      "affiliation": "QWANT RESEARCH - 7 Rue Spontini, 75116 Paris, France", 
      "@type": "Person", 
      "name": "Servan, Christophe"
    }
  ], 
  "headline": "Using Whole Document Context in Neural Machine Translation", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2019-11-02", 
  "url": "https://zenodo.org/record/3525020", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3525020", 
  "@id": "https://doi.org/10.5281/zenodo.3525020", 
  "@type": "ScholarlyArticle", 
  "name": "Using Whole Document Context in Neural Machine Translation"
}