  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.
