November 2, 2019 Conference paper Open Access

Macé, Valentin; Servan, Christophe

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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3525020", 
  "language": "eng", 
  "title": "Using Whole Document Context in Neural Machine Translation", 
  "issued": {
    "date-parts": [
      [
        2019, 
        11, 
        2
      ]
    ]
  }, 
  "abstract": "<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>", 
  "author": [
    {
      "family": "Mac\u00e9, Valentin"
    }, 
    {
      "family": "Servan, Christophe"
    }
  ], 
  "type": "paper-conference", 
  "id": "3525020"
}