  Although attention-based Neural Machine Translation have achieved great
success, attention-mechanism cannot capture the entire meaning of the source
sentence because the attention mechanism generates a target word depending
heavily on the relevant parts of the source sentence. The report of earlier
studies has introduced a latent variable to capture the entire meaning of
sentence and achieved improvement on attention-based Neural Machine
Translation. We follow this approach and we believe that the capturing meaning
of sentence benefits from image information because human beings understand the
meaning of language not only from textual information but also from perceptual
information such as that gained from vision. As described herein, we propose a
neural machine translation model that introduces a continuous latent variable
containing an underlying semantic extracted from texts and images. Our model,
which can be trained end-to-end, requires image information only when training.
Experiments conducted with an English--German translation task show that our
model outperforms over the baseline.
