  This paper explores new evaluation perspectives for image captioning and
introduces a noun translation task that achieves comparative image caption
generation performance by translating from a set of nouns to captions. This
implies that in image captioning, all word categories other than nouns can be
evoked by a powerful language model without sacrificing performance on n-gram
precision. The paper also investigates lower and upper bounds of how much
individual word categories in the captions contribute to the final BLEU score.
A large possible improvement exists for nouns, verbs, and prepositions.
