  Images have become an integral part of online media. This has enhanced
self-expression and the dissemination of knowledge, but it poses serious
accessibility challenges. Adequate textual descriptions are rare. Captions are
more abundant, but they do not consistently provide the needed descriptive
details, and systems trained on such texts inherit these shortcomings. To
address this, we introduce the publicly available Wikipedia-based corpus
Concadia, which consists of 96,918 images with corresponding English-language
descriptions, captions, and surrounding context. We use Concadia to further
characterize the commonalities and differences between descriptions and
captions, and this leads us to the hypothesis that captions, while not
substitutes for descriptions, can provide a useful signal for creating
effective descriptions. We substantiate this hypothesis by showing that image
captioning systems trained on Concadia benefit from having caption embeddings
as part of their inputs. These experiments also begin to show how Concadia can
be a powerful tool in addressing the underlying accessibility issues posed by
image data.
