  We introduce the first dataset for sequential vision-to-language, and explore
how this data may be used for the task of visual storytelling. The first
release of this dataset, SIND v.1, includes 81,743 unique photos in 20,211
sequences, aligned to both descriptive (caption) and story language. We
establish several strong baselines for the storytelling task, and motivate an
automatic metric to benchmark progress. Modelling concrete description as well
as figurative and social language, as provided in this dataset and the
storytelling task, has the potential to move artificial intelligence from basic
understandings of typical visual scenes towards more and more human-like
understanding of grounded event structure and subjective expression.
