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We propose an end-to-end model which generates captions for images embeddedin news articles. News images present two key challenges: they rely onreal-world knowledge, especially about named entities; and they typically havelinguistically rich captions that include uncommon words. We address the firstchallenge by associating words in the caption with faces and objects in theimage, via a multi-modal, multi-head attention mechanism. We tackle the secondchallenge with a state-of-the-art transformer language model that usesbyte-pair-encoding to generate captions as a sequence of word parts. On theGoodNews dataset, our model outperforms the previous state of the art by afactor of four in CIDEr score (13 to 54). This performance gain comes from aunique combination of language models, word representation, image embeddings,face embeddings, object embeddings, and improvements in neural network design.We also introduce the NYTimes800k dataset which is 70% larger than GoodNews,has higher article quality, and includes the locations of images withinarticles as an additional contextual cue.

Transform and Tell: Entity-Aware News Image Captioning