  Semantic embeddings have advanced the state of the art for countless natural
language processing tasks, and various extensions to multimodal domains, such
as visual-semantic embeddings, have been proposed. While the power of
visual-semantic embeddings comes from the distillation and enrichment of
information through machine learning, their inner workings are poorly
understood and there is a shortage of analysis tools. To address this problem,
we generalize the notion of probing tasks to the visual-semantic case. To this
end, we (i) discuss the formalization of probing tasks for embeddings of
image-caption pairs, (ii) define three concrete probing tasks within our
general framework, (iii) train classifiers to probe for those properties, and
(iv) compare various state-of-the-art embeddings under the lens of the proposed
probing tasks. Our experiments reveal an up to 12% increase in accuracy on
visual-semantic embeddings compared to the corresponding unimodal embeddings,
which suggest that the text and image dimensions represented in the former do
complement each other.
