Conference paper Open Access

# Using Whole Document Context in Neural Machine Translation

Macé, Valentin; Servan, Christophe

### Citation Style Language JSON Export

{
"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"
}
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