  We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual
information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM)
build vector-based word representations by learning to predict linguistic
contexts in text corpora. However, for a restricted set of words, the models
are also exposed to visual representations of the objects they denote
(extracted from natural images), and must predict linguistic and visual
features jointly. The MMSKIP-GRAM models achieve good performance on a variety
of semantic benchmarks. Moreover, since they propagate visual information to
all words, we use them to improve image labeling and retrieval in the zero-shot
setup, where the test concepts are never seen during model training. Finally,
the MMSKIP-GRAM models discover intriguing visual properties of abstract words,
paving the way to realistic implementations of embodied theories of meaning.
