  There has been significant interest recently in learning multilingual word
embeddings -- in which semantically similar words across languages have similar
embeddings. State-of-the-art approaches have relied on expensive labeled data,
which is unavailable for low-resource languages, or have involved post-hoc
unification of monolingual embeddings. In the present paper, we investigate the
efficacy of multilingual embeddings learned from weakly-supervised image-text
data. In particular, we propose methods for learning multilingual embeddings
using image-text data, by enforcing similarity between the representations of
the image and that of the text. Our experiments reveal that even without using
any expensive labeled data, a bag-of-words-based embedding model trained on
image-text data achieves performance comparable to the state-of-the-art on
crosslingual semantic similarity tasks.
