Before revision:  Existing multimodal neural machine translation  (MNMT) methods achieve knowledge transfer by enforcing one encoder to learn shared representation across textual and visual modalities. However, the training and inference process heavily relies on well-aligned bilingual sentence - image triplets as input, which are often limited in quantity .
After revision:  Existing multimodal neural machine translation (MNMT) require triplets of bilingual sentence - image for training and tuples of source sentence - image triplets as input, which are often limited in quantity .