The worst case of N-gram problem is that the model directly generated the same frequent captions in the training set (Devlin et al., 2015a).
the capability of generating novel captions for an image that is not seen in the training set indicates that the generator is able to understand a given image better instead of simply generating frequent Ngrams found in the training set.