We find that a model trained in the aligned setting (De+En) is better than a model trained in the disjoint setting (De+COCO), as shown in Table  2 . This finding contradicts the conclusion of<cite> Kádár et al. (2018)</cite> , who claimed that the aligned and disjoint conditions lead to comparable performance. This is most likely because the disjoint setting in<cite> Kádár et al. (2018)</cite> is artificial, in the sense that they used different 50% subsets of M30K. In our experiments the disjoint image-caption sets are real, in the sense that we trained the models on the two different datasets.