  We present a document-level neural machine translation model which takes both
source and target document context into account using memory networks. We model
the problem as a structured prediction problem with interdependencies among the
observed and hidden variables, i.e., the source sentences and their unobserved
target translations in the document. The resulting structured prediction
problem is tackled with a neural translation model equipped with two memory
components, one each for the source and target side, to capture the documental
interdependencies. We train the model end-to-end, and propose an iterative
decoding algorithm based on block coordinate descent. Experimental results of
English translations from French, German, and Estonian documents show that our
model is effective in exploiting both source and target document context, and
statistically significantly outperforms the previous work in terms of BLEU and
METEOR.
