In other words, there exist a few directions in the embedding space which disproportionately explain the variance. It is clear that the visual features in the case of How2 dataset are much more dominated by the "common" dimensions, when compared to the Multi30k dataset. Further, this analysis is still at the sentence level, i.e. the visual features are much less discriminative among individual sentences, further aggravating the problem at the token level. This suggests that the existing visual features aren't sufficient enough to expect benefits from the visual modality in NMT, since they won't provide discriminativeness among the vocabulary elements at the token level during prediction. 
 Question: What is result of their Principal Component Analysis?
existing visual features aren't sufficient enough to expect benefits from the visual modality in NMT