  In Multimodal Neural Machine Translation (MNMT), a neural model generates a
translated sentence that describes an image, given the image itself and one
source descriptions in English. This is considered as the multimodal image
caption translation task. The images are processed with Convolutional Neural
Network (CNN) to extract visual features exploitable by the translation model.
So far, the CNNs used are pre-trained on object detection and localization
task. We hypothesize that richer architecture, such as dense captioning models,
may be more suitable for MNMT and could lead to improved translations. We
extend this intuition to the word-embeddings, where we compute both linguistic
and visual representation for our corpus vocabulary. We combine and compare
different confi
