  In translation, considering the document as a whole can help to resolve
ambiguities and inconsistencies. In this paper, we propose a cross-sentence
context-aware approach and investigate the influence of historical contextual
information on the performance of neural machine translation (NMT). First, this
history is summarized in a hierarchical way. We then integrate the historical
representation into NMT in two strategies: 1) a warm-start of encoder and
decoder states, and 2) an auxiliary context source for updating decoder states.
Experimental results on a large Chinese-English translation task show that our
approach significantly improves upon a strong attention-based NMT system by up
to +2.1 BLEU points.
