  We introduce a Multi-modal Neural Machine Translation model in which a
doubly-attentive decoder naturally incorporates spatial visual features
obtained using pre-trained convolutional neural networks, bridging the gap
between image description and translation. Our decoder learns to attend to
source-language words and parts of an image independently by means of two
separate attention mechanisms as it generates words in the target language. We
find that our model can efficiently exploit not just back-translated in-domain
multi-modal data but also large general-domain text-only MT corpora. We also
report state-of-the-art results on the Multi30k data set.
