Conference paper Open Access

Using Whole Document Context in Neural Machine Translation

Macé, Valentin; Servan, Christophe

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Macé, Valentin</dc:creator>
  <dc:creator>Servan, Christophe</dc:creator>
  <dc:description>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.</dc:description>
  <dc:title>Using Whole Document Context in Neural Machine Translation</dc:title>
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